R Notebook
library(tidyverse)## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr## Warning: package 'tidyr' was built under R version 3.4.2## Warning: package 'purrr' was built under R version 3.4.2## Warning: package 'dplyr' was built under R version 3.4.2## Conflicts with tidy packages ----------------------------------------------## filter(): dplyr, stats
## lag(): dplyr, statslibrary(ggplot2)
library(dplyr)
library(ggthemes)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combineMotivation
We are interested in exploring domestic health policy through the lens of county-level health rankings, mortality data, and other relevant determinants of health. Across the United States, there is broad diversity and variation in health status, access to care, and health outcomes.
We are curious about the decline of life expectancy in the US (for the first time since the 1990s), and which groups of people (by location, race, income level, or other factors) experienced different changes in mortality and by what cause of death. This analysis could provide insights that could guide targeted health interventions for specific communities in the US.
Project Development
We wanted to dive further into mortality and attempt to understand the factors that contribute to high mortality rates in certain counties. With this in mind, we were curious about performing Principal Component Analysis (PCA) for mortality. We found other literature where this method had been employed to understand mortality in developing countries, cardiovascular deaths among Native Americans, and malaria cases in Ghana.
Research Questions:
- Can we use PCA to understand the dominating factors that drive county-level mortality rates in the United States?
- Can applying PCA help to inform trends in mortality rates at the county level that can be used to tailor context-specific interventions?
Data Cleaning
Sources for Data Analysis:
For this project, we used the following data sources (all open access!):
US county health rankings
US county-level mortality data
Cleaning and Reshaping Life Expectancy and Mortality Data
Data was downloaded from IHME, the header cells were removed, formatting was removed, and it was saved into a .csv file for life expectancy and for mortality.
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithslibrary(rio)Life Expectancy Data:
finalLE <- import("ledata.csv")Mortality Data:
State-level mortality rates were downloaded from IHME and merged. Data were only available in state files.
Alaska <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_ALASKA_Y2017M05D19.csv" )
Alabama <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_ALABAMA_Y2017M05D19.csv" )
Arizona <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_ARIZONA_Y2017M05D19.csv" )
Arkansas <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_ARKANSAS_Y2017M05D19.csv" )
California <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_CALIFORNIA_Y2017M05D19.csv" )
COLORADO <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_COLORADO_Y2017M05D19.csv" )
CONNECTICUT <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_CONNECTICUT_Y2017M05D19.csv" )
DELAWARE <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_DELAWARE_Y2017M05D19.csv" )
DC <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_DISTRICT_OF_COLUMBIA_Y2017M05D19.csv" )
FLORIDA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_FLORIDA_Y2017M05D19.csv" )
GEORGIA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_GEORGIA_Y2017M05D19.csv" )
HAWAII <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_HAWAII_Y2017M05D19.csv" )
IDAHO <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_IDAHO_Y2017M05D19.csv" )
ILLINOIS <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_ILLINOIS_Y2017M05D19.csv" )
INDIANA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_INDIANA_Y2017M05D19.csv" )
IOWA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_IOWA_Y2017M05D19.csv" )
KANSAS <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_KANSAS_Y2017M05D19.csv" )
KENTUCKY <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_KENTUCKY_Y2017M05D19.csv" )
LOUISIANA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_LOUISIANA_Y2017M05D19.csv" )
MAINE <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_MAINE_Y2017M05D19.csv" )
MARYLAND <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_MARYLAND_Y2017M05D19.csv" )
MASSACHUSETTS <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_MASSACHUSETTS_Y2017M05D19.csv" )
MIGHIGAN <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_MICHIGAN_Y2017M05D19.csv" )
MINNESOTA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_MINNESOTA_Y2017M05D19.csv" )
MISSISSIPPI <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_MISSISSIPPI_Y2017M05D19.csv" )
MISSOURI <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_MISSOURI_Y2017M05D19.csv" )
MONTANA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_MONTANA_Y2017M05D19.csv" )
NEBRASKA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_NEBRASKA_Y2017M05D19.csv" )
NEVADA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_NEVADA_Y2017M05D19.csv" )
NEWHAMPSHIRE <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_NEW_HAMPSHIRE_Y2017M05D19.csv" )
NEWJERSEY <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_NEW_JERSEY_Y2017M05D19.csv" )
NEWMEXICO <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_NEW_MEXICO_Y2017M05D19.csv" )
NEWYORK <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_NEW_YORK_Y2017M05D19.csv" )
NORTHCAROLINA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_NORTH_CAROLINA_Y2017M05D19.csv" )
NORTHDAKOTA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_NORTH_DAKOTA_Y2017M05D19.csv" )
OHIO <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_OHIO_Y2017M05D19.csv" )
OKLAHOMA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_OKLAHOMA_Y2017M05D19.csv" )
OREGON <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_OREGON_Y2017M05D19.csv" )
PENNSYLVANIA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_PENNSYLVANIA_Y2017M05D19.csv" )
RHODEISLAND <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_RHODE_ISLAND_Y2017M05D19.csv" )
SOUTHCAROLINA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_SOUTH_CAROLINA_Y2017M05D19.csv" )
SOUTHDAKOTA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_SOUTH_DAKOTA_Y2017M05D19.csv" )
TENNESSEE <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_TENNESSEE_Y2017M05D19.csv" )
TEXAS <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_TEXAS_Y2017M05D19.csv" )
UTAH <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_UTAH_Y2017M05D19.csv" )
VERMONT <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_VERMONT_Y2017M05D19.csv" )
VIRGINIA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_VIRGINIA_Y2017M05D19.csv" )
WASHINGTON <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_WASHINGTON_Y2017M05D19.csv" )
WESTVIRGINIA <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_WEST_VIRGINIA_Y2017M05D19.csv" )
WISCONSIN <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_WISCONSIN_Y2017M05D19.csv" )
WYOMING <- import(file="IHMEdata/IHME_USA_COUNTY_MORTALITY_RATES_1980_2014_WYOMING_Y2017M05D19.csv" )AllMortality <- rbind(Alaska, Alabama, Arizona, Arkansas, California, COLORADO, CONNECTICUT, DELAWARE, DC, FLORIDA, GEORGIA, HAWAII, IDAHO, ILLINOIS, INDIANA, IOWA, KANSAS, KENTUCKY, LOUISIANA, MAINE, MASSACHUSETTS, MARYLAND, MIGHIGAN, MINNESOTA, MISSISSIPPI, MISSOURI, MONTANA, NEBRASKA, NEVADA, NEWHAMPSHIRE, NEWYORK, NEWJERSEY, NEWMEXICO, NORTHCAROLINA, NORTHDAKOTA, OHIO, OKLAHOMA, OREGON, PENNSYLVANIA, RHODEISLAND, SOUTHCAROLINA, SOUTHDAKOTA, TENNESSEE, TEXAS, UTAH, VERMONT, VIRGINIA, WASHINGTON, WESTVIRGINIA, WISCONSIN, WYOMING) #merge all 51 states
AllMortality <- dplyr::filter(AllMortality, year_id >= 2010) # keep only data from 2010 - 2014
save(AllMortality, file="AllMortality.Rda")Independent Variables from the RWJF
Prior to uploading to R, Excel files were downloaded from the RWJF Country Health ranking website for the year 2014. Files were examined in Excel and formatted correctly to make compatible with R. This included deleting rows above the header and identifying andrenaming variables relevant to the analysis.
IV_2014_1 <- import("IV_2014_1_csv.csv")
IV_2014_1 <- IV_2014_1[, -c(39:81)]
IV_2014_1$year <- NA
IV_2014_1 <- IV_2014_1[,c(1:3,39,4:38)]
IV_2014_1$year <- 2014
IV <- bind_rows(IV_2014_1) #Initial binding rows
str(IV_2014_1$X..Physically.Inactive)## NULLnames(IV_2014_1)[names(IV_2014_1) == 'X..Physically.Inactive'] <- 'inactive_percent' #Rename 2014 "Physically.Inactive" to "inactive_percent"
IV <- bind_rows(IV_2014_1)
str(IV)## 'data.frame': 3141 obs. of 39 variables:
## $ FIPS : int 1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 ...
## $ State : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ County : chr "Autauga" "Baldwin" "Barbour" "Bibb" ...
## $ year : num 2014 2014 2014 2014 2014 ...
## $ N_RankedCounties : int 67 67 67 67 67 67 67 67 67 67 ...
## $ HO_rank : chr "10" "2" "32" "51" ...
## $ HO_quart : chr "1" "1" "2" "4" ...
## $ HF_rank : chr "7" "6" "58" "38" ...
## $ HF_quart : chr "1" "1" "4" "3" ...
## $ MORT_rank : chr "7" "5" "22" "54" ...
## $ MORT_quart : chr "1" "1" "2" "4" ...
## $ MORB_rank : chr "21" "2" "50" "51" ...
## $ MORT_quart.1 : chr "2" "1" "3" "4" ...
## $ HB_rank : chr "15" "9" "64" "42" ...
## $ HB_quart : chr "1" "1" "4" "3" ...
## $ CC_rank : chr "10" "8" "36" "33" ...
## $ CC_quart : chr "1" "1" "3" "2" ...
## $ SE_rank : chr "6" "7" "60" "40" ...
## $ SE_quart : chr "1" "1" "4" "3" ...
## $ PE_rank : chr "42" "49" "60" "19" ...
## $ PE_quart : chr "3" "3" "4" "2" ...
## $ Deaths : int 729 2325 422 390 826 192 389 2042 643 470 ...
## $ Population : int 55514 190790 27201 22597 57826 10474 20307 117296 34064 26021 ...
## $ YPPL_rate : int 8376 7770 9458 11544 8506 9884 11006 11097 10368 9409 ...
## $ Fair_percent : int 23 13 23 18 24 23 20 23 39 24 ...
## $ PPH_days : num 5.1 3.3 4.8 4.7 5.9 4.2 3.9 5.2 5 6.1 ...
## $ PMH_days : num 3.6 3.8 4.3 5.1 3.9 NA 3.5 5.1 4.3 5.2 ...
## $ LBW_percent : num 9.3 8.9 12.3 12.7 7.7 13.7 10.4 9 11.7 8.9 ...
## $ smoking_percent : int 22 21 25 26 21 38 31 24 24 24 ...
## $ Obese_percent : int 31 27 37 34 30 42 38 33 35 31 ...
## $ % Physically Inactive: int 29 28 35 37 35 33 37 33 39 34 ...
## $ Binge_percent : int 17 18 14 11 6 NA 11 12 NA 15 ...
## $ CHL_100000 : int 447 302 874 466 175 1404 639 663 766 269 ...
## $ TeenB_rate : int 39 46 74 49 48 85 61 51 63 52 ...
## $ Uninsure_percent : int 14 17 19 16 18 16 16 17 18 17 ...
## $ PCP_rate : int 38 75 44 22 19 28 34 67 38 27 ...
## $ diab_rate : int 12 12 14 11 14 18 16 14 15 14 ...
## $ unemploy_rate : num 6.5 6.8 11.2 7.6 6.2 13.4 10.9 7.6 9.3 7.1 ...
## $ Childpov_percent : int 19 21 47 27 23 43 42 31 35 30 ...names(IV)[names(IV) == 'ï..FIPS'] <- 'FIPS'
names(IV)[names(IV) == 'MORT_quart.1'] <- 'MORB_quart'
#Convert rows to numerics
IV$HO_rank <- as.numeric(IV$HO_rank)## Warning: NAs introduced by coercionIV$HO_quart <- as.numeric(as.character(IV$HO_quart))## Warning: NAs introduced by coercionIV$HF_rank <- as.numeric(IV$HF_rank)## Warning: NAs introduced by coercionIV$HF_quart <- as.numeric(as.character(IV$HF_quart))## Warning: NAs introduced by coercionIV$MORT_rank <- as.numeric(IV$MORT_rank)## Warning: NAs introduced by coercionIV$MORT_quart <- as.numeric(as.character(IV$MORT_quart))## Warning: NAs introduced by coercionIV$MORB_rank <- as.numeric(IV$MORB_rank)## Warning: NAs introduced by coercionIV$MORB_quart <- as.numeric(as.character(IV$MORB_quart))## Warning: NAs introduced by coercionIV$HB_rank <- as.numeric(IV$HB_rank)## Warning: NAs introduced by coercionIV$HB_quart <- as.numeric(as.character(IV$HB_quart))## Warning: NAs introduced by coercionIV$CC_rank <- as.numeric(IV$CC_rank)## Warning: NAs introduced by coercionIV$CC_quart <- as.numeric(as.character(IV$CC_quart))## Warning: NAs introduced by coercionIV$SE_rank <- as.numeric(IV$SE_rank)## Warning: NAs introduced by coercionIV$SE_quart <- as.numeric(as.character(IV$SE_quart))## Warning: NAs introduced by coercionIV$PE_rank <- as.numeric(IV$PE_rank)## Warning: NAs introduced by coercionIV$PE_quart <- as.numeric(as.character(IV$PE_quart))## Warning: NAs introduced by coercion#Examine structure of IV
str(IV)## 'data.frame': 3141 obs. of 39 variables:
## $ FIPS : int 1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 ...
## $ State : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ County : chr "Autauga" "Baldwin" "Barbour" "Bibb" ...
## $ year : num 2014 2014 2014 2014 2014 ...
## $ N_RankedCounties : int 67 67 67 67 67 67 67 67 67 67 ...
## $ HO_rank : num 10 2 32 51 12 39 27 40 48 26 ...
## $ HO_quart : num 1 1 2 4 1 3 2 3 3 2 ...
## $ HF_rank : num 7 6 58 38 13 67 60 29 51 24 ...
## $ HF_quart : num 1 1 4 3 1 4 4 2 4 2 ...
## $ MORT_rank : num 7 5 22 54 9 28 46 50 34 20 ...
## $ MORT_quart : num 1 1 2 4 1 2 3 3 2 2 ...
## $ MORB_rank : num 21 2 50 51 20 54 12 35 59 42 ...
## $ MORB_quart : num 2 1 3 4 2 4 1 3 4 3 ...
## $ HB_rank : num 15 9 64 42 7 67 62 29 52 33 ...
## $ HB_quart : num 1 1 4 3 1 4 4 2 4 2 ...
## $ CC_rank : num 10 8 36 33 44 46 56 29 31 37 ...
## $ CC_quart : num 1 1 3 2 3 3 4 2 2 3 ...
## $ SE_rank : num 6 7 60 40 9 63 59 32 51 22 ...
## $ SE_quart : num 1 1 4 3 1 4 4 2 4 2 ...
## $ PE_rank : num 42 49 60 19 21 52 67 29 46 16 ...
## $ PE_quart : num 3 3 4 2 2 4 4 2 3 1 ...
## $ Deaths : int 729 2325 422 390 826 192 389 2042 643 470 ...
## $ Population : int 55514 190790 27201 22597 57826 10474 20307 117296 34064 26021 ...
## $ YPPL_rate : int 8376 7770 9458 11544 8506 9884 11006 11097 10368 9409 ...
## $ Fair_percent : int 23 13 23 18 24 23 20 23 39 24 ...
## $ PPH_days : num 5.1 3.3 4.8 4.7 5.9 4.2 3.9 5.2 5 6.1 ...
## $ PMH_days : num 3.6 3.8 4.3 5.1 3.9 NA 3.5 5.1 4.3 5.2 ...
## $ LBW_percent : num 9.3 8.9 12.3 12.7 7.7 13.7 10.4 9 11.7 8.9 ...
## $ smoking_percent : int 22 21 25 26 21 38 31 24 24 24 ...
## $ Obese_percent : int 31 27 37 34 30 42 38 33 35 31 ...
## $ % Physically Inactive: int 29 28 35 37 35 33 37 33 39 34 ...
## $ Binge_percent : int 17 18 14 11 6 NA 11 12 NA 15 ...
## $ CHL_100000 : int 447 302 874 466 175 1404 639 663 766 269 ...
## $ TeenB_rate : int 39 46 74 49 48 85 61 51 63 52 ...
## $ Uninsure_percent : int 14 17 19 16 18 16 16 17 18 17 ...
## $ PCP_rate : int 38 75 44 22 19 28 34 67 38 27 ...
## $ diab_rate : int 12 12 14 11 14 18 16 14 15 14 ...
## $ unemploy_rate : num 6.5 6.8 11.2 7.6 6.2 13.4 10.9 7.6 9.3 7.1 ...
## $ Childpov_percent : int 19 21 47 27 23 43 42 31 35 30 ...save(IV,file="Predictors_1.Rda")Z-Scores
The predictors were theh converted to z-scores to standardize the ranks.
#loading 2014 data
RWJFMeasures_2014 <- import("IV_2014_healthrank.csv")
str(RWJFMeasures_2014)## 'data.frame': 3141 obs. of 48 variables:
## $ FIPS : int 1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 ...
## $ State : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ County : chr "Autauga" "Baldwin" "Barbour" "Bibb" ...
## $ N_RankedCounties : int 67 67 67 67 67 67 67 67 67 67 ...
## $ HO_rank : chr "10" "2" "32" "51" ...
## $ HF_rank : chr "7" "6" "58" "38" ...
## $ MORT_rank : chr "7" "5" "22" "54" ...
## $ MORB_rank : chr "21" "2" "50" "51" ...
## $ HB_rank : chr "15" "9" "64" "42" ...
## $ CC_rank : chr "10" "8" "36" "33" ...
## $ SE_rank : chr "6" "7" "60" "40" ...
## $ PE_rank : chr "42" "49" "60" "19" ...
## $ Deaths : int 729 2325 422 390 826 192 389 2042 643 470 ...
## $ Population : int 55514 190790 27201 22597 57826 10474 20307 117296 34064 26021 ...
## $ YPPL_rate : int 8376 7770 9458 11544 8506 9884 11006 11097 10368 9409 ...
## $ Fair_percent : int 23 13 23 18 24 23 20 23 39 24 ...
## $ PPH_days : num 5.1 3.3 4.8 4.7 5.9 4.2 3.9 5.2 5 6.1 ...
## $ PMH_days : num 3.6 3.8 4.3 5.1 3.9 NA 3.5 5.1 4.3 5.2 ...
## $ LBW_percent : num 9.3 8.9 12.3 12.7 7.7 13.7 10.4 9 11.7 8.9 ...
## $ smoking_percent : int 22 21 25 26 21 38 31 24 24 24 ...
## $ Obese_percent : int 31 27 37 34 30 42 38 33 35 31 ...
## $ FoodEnv_Index : int 7 8 5 8 9 4 6 7 6 8 ...
## $ inactive_percent : int 29 28 35 37 35 33 37 33 39 34 ...
## $ Exercise_access_percent: int 59 58 11 18 15 29 47 41 34 15 ...
## $ Binge_percent : int 17 18 14 11 6 NA 11 12 NA 15 ...
## $ CHL_100000 : int 447 302 874 466 175 1404 639 663 766 269 ...
## $ TeenB_rate : int 39 46 74 49 48 85 61 51 63 52 ...
## $ Uninsure_percent : int 14 17 19 16 18 16 16 17 18 17 ...
## $ PCP_rate : int 38 75 44 22 19 28 34 67 38 27 ...
## $ dentist_rate : int 27 44 33 16 19 16 29 46 23 15 ...
## $ MHP_rate : int 2 46 3 NA 3 8 19 63 NA 15 ...
## $ acscrate : int 75 54 101 90 91 91 128 92 89 100 ...
## $ hbac_rate : int 87 80 88 83 84 81 78 80 84 88 ...
## $ mammo_percent : num 66.6 67.1 64.5 62 62.8 48.5 58.1 55.2 65.8 59.6 ...
## $ Hsgrad_rate : int 80 74 61 73 81 73 72 79 80 72 ...
## $ college_percent : num 54.7 61.8 41.4 44.2 46.3 36 46.2 50.8 47.1 53.5 ...
## $ unemploy_rate : num 6.5 6.8 11.2 7.6 6.2 13.4 10.9 7.6 9.3 7.1 ...
## $ Childpov_percent : int 19 21 47 27 23 43 42 31 35 30 ...
## $ social_assoc_rate : int 24 19 18 29 14 NA 28 22 33 23 ...
## $ singleparent_percent : int 30 28 56 39 25 65 49 39 52 26 ...
## $ violentcrime_rate : int 303 213 154 254 164 334 453 553 576 336 ...
## $ injurydeathrate : int 70 76 53 98 89 103 71 78 78 81 ...
## $ airpollution : num 12.9 13.1 12.6 12.9 12.7 12.8 12.8 12.9 13 12.9 ...
## $ drinkingwater_viol : int 0 4 32 0 3 15 25 0 0 0 ...
## $ severehousing_percent : int 12 13 17 10 13 14 17 14 17 10 ...
## $ driving_alone_percent : int 88 83 83 82 80 84 89 84 84 81 ...
## $ longcommute_percent : int 42 35 32 50 60 45 32 27 33 42 ...
## $ alc_drivingdeath : int 25 32 48 28 23 40 15 20 25 30 ...names(RWJFMeasures_2014)[names(RWJFMeasures_2014) == 'ï..FIPS'] <- 'FIPS'
RWJFMeasures_2014$HO_rank <- as.numeric(as.character(RWJFMeasures_2014$HO_rank))## Warning: NAs introduced by coercionRWJFMeasures_2014$HF_rank <- as.numeric(as.character(RWJFMeasures_2014$HF_rank))## Warning: NAs introduced by coercionRWJFMeasures_2014$MORT_rank <- as.numeric(as.character(RWJFMeasures_2014$MORT_rank))## Warning: NAs introduced by coercionRWJFMeasures_2014$MORB_rank <- as.numeric(as.character(RWJFMeasures_2014$MORB_rank))## Warning: NAs introduced by coercionRWJFMeasures_2014$HB_rank <- as.numeric(as.character(RWJFMeasures_2014$HB_rank))## Warning: NAs introduced by coercionRWJFMeasures_2014$CC_rank <- as.numeric(as.character(RWJFMeasures_2014$CC_rank))## Warning: NAs introduced by coercionRWJFMeasures_2014$SE_rank <- as.numeric(as.character(RWJFMeasures_2014$SE_rank))## Warning: NAs introduced by coercionRWJFMeasures_2014$PE_rank <- as.numeric(as.character(RWJFMeasures_2014$PE_rank))## Warning: NAs introduced by coercionstr(RWJFMeasures_2014)## 'data.frame': 3141 obs. of 48 variables:
## $ FIPS : int 1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 ...
## $ State : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ County : chr "Autauga" "Baldwin" "Barbour" "Bibb" ...
## $ N_RankedCounties : int 67 67 67 67 67 67 67 67 67 67 ...
## $ HO_rank : num 10 2 32 51 12 39 27 40 48 26 ...
## $ HF_rank : num 7 6 58 38 13 67 60 29 51 24 ...
## $ MORT_rank : num 7 5 22 54 9 28 46 50 34 20 ...
## $ MORB_rank : num 21 2 50 51 20 54 12 35 59 42 ...
## $ HB_rank : num 15 9 64 42 7 67 62 29 52 33 ...
## $ CC_rank : num 10 8 36 33 44 46 56 29 31 37 ...
## $ SE_rank : num 6 7 60 40 9 63 59 32 51 22 ...
## $ PE_rank : num 42 49 60 19 21 52 67 29 46 16 ...
## $ Deaths : int 729 2325 422 390 826 192 389 2042 643 470 ...
## $ Population : int 55514 190790 27201 22597 57826 10474 20307 117296 34064 26021 ...
## $ YPPL_rate : int 8376 7770 9458 11544 8506 9884 11006 11097 10368 9409 ...
## $ Fair_percent : int 23 13 23 18 24 23 20 23 39 24 ...
## $ PPH_days : num 5.1 3.3 4.8 4.7 5.9 4.2 3.9 5.2 5 6.1 ...
## $ PMH_days : num 3.6 3.8 4.3 5.1 3.9 NA 3.5 5.1 4.3 5.2 ...
## $ LBW_percent : num 9.3 8.9 12.3 12.7 7.7 13.7 10.4 9 11.7 8.9 ...
## $ smoking_percent : int 22 21 25 26 21 38 31 24 24 24 ...
## $ Obese_percent : int 31 27 37 34 30 42 38 33 35 31 ...
## $ FoodEnv_Index : int 7 8 5 8 9 4 6 7 6 8 ...
## $ inactive_percent : int 29 28 35 37 35 33 37 33 39 34 ...
## $ Exercise_access_percent: int 59 58 11 18 15 29 47 41 34 15 ...
## $ Binge_percent : int 17 18 14 11 6 NA 11 12 NA 15 ...
## $ CHL_100000 : int 447 302 874 466 175 1404 639 663 766 269 ...
## $ TeenB_rate : int 39 46 74 49 48 85 61 51 63 52 ...
## $ Uninsure_percent : int 14 17 19 16 18 16 16 17 18 17 ...
## $ PCP_rate : int 38 75 44 22 19 28 34 67 38 27 ...
## $ dentist_rate : int 27 44 33 16 19 16 29 46 23 15 ...
## $ MHP_rate : int 2 46 3 NA 3 8 19 63 NA 15 ...
## $ acscrate : int 75 54 101 90 91 91 128 92 89 100 ...
## $ hbac_rate : int 87 80 88 83 84 81 78 80 84 88 ...
## $ mammo_percent : num 66.6 67.1 64.5 62 62.8 48.5 58.1 55.2 65.8 59.6 ...
## $ Hsgrad_rate : int 80 74 61 73 81 73 72 79 80 72 ...
## $ college_percent : num 54.7 61.8 41.4 44.2 46.3 36 46.2 50.8 47.1 53.5 ...
## $ unemploy_rate : num 6.5 6.8 11.2 7.6 6.2 13.4 10.9 7.6 9.3 7.1 ...
## $ Childpov_percent : int 19 21 47 27 23 43 42 31 35 30 ...
## $ social_assoc_rate : int 24 19 18 29 14 NA 28 22 33 23 ...
## $ singleparent_percent : int 30 28 56 39 25 65 49 39 52 26 ...
## $ violentcrime_rate : int 303 213 154 254 164 334 453 553 576 336 ...
## $ injurydeathrate : int 70 76 53 98 89 103 71 78 78 81 ...
## $ airpollution : num 12.9 13.1 12.6 12.9 12.7 12.8 12.8 12.9 13 12.9 ...
## $ drinkingwater_viol : int 0 4 32 0 3 15 25 0 0 0 ...
## $ severehousing_percent : int 12 13 17 10 13 14 17 14 17 10 ...
## $ driving_alone_percent : int 88 83 83 82 80 84 89 84 84 81 ...
## $ longcommute_percent : int 42 35 32 50 60 45 32 27 33 42 ...
## $ alc_drivingdeath : int 25 32 48 28 23 40 15 20 25 30 ...RWJFMeasures_2014$HO_rank <- ave(RWJFMeasures_2014$HO_rank, RWJFMeasures_2014$State, FUN=scale)
#Get zscores
RWJFMeasures_2014$HF_rank <- ave(RWJFMeasures_2014$HF_rank, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$MORT_rank <- ave(RWJFMeasures_2014$MORT_rank, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$MORB_rank <- ave(RWJFMeasures_2014$MORB_rank, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$HB_rank <- ave(RWJFMeasures_2014$HB_rank, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$CC_rank <- ave(RWJFMeasures_2014$CC_rank, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$SE_rank <- ave(RWJFMeasures_2014$SE_rank, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$PE_rank <- ave(RWJFMeasures_2014$PE_rank, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014 <- add_column(RWJFMeasures_2014, deaths_zscore="Deaths", .after = "Deaths")
RWJFMeasures_2014 <- add_column(RWJFMeasures_2014, pop_zscore="Population", .after = "Population")
RWJFMeasures_2014$deaths_zscore <- ave(RWJFMeasures_2014$Deaths, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$pop_zscore <- ave(RWJFMeasures_2014$Population, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$YPPL_rate <- ave(RWJFMeasures_2014$YPPL_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$Fair_percent <- ave(RWJFMeasures_2014$Fair_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$PPH_days <- ave(RWJFMeasures_2014$PPH_days, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$PMH_days <- ave(RWJFMeasures_2014$PMH_days, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$LBW_percent <- ave(RWJFMeasures_2014$LBW_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$smoking_percent <- ave(RWJFMeasures_2014$smoking_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$Obese_percent <- ave(RWJFMeasures_2014$Obese_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$FoodEnv_Index <- ave(RWJFMeasures_2014$FoodEnv_Index, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$inactive_percent <- ave(RWJFMeasures_2014$inactive_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$Exercise_access_percent <- ave(RWJFMeasures_2014$Exercise_access_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$Binge_percent <- ave(RWJFMeasures_2014$Binge_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$CHL_100000 <- ave(RWJFMeasures_2014$CHL_100000, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$TeenB_rate <- ave(RWJFMeasures_2014$TeenB_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$Uninsure_percent <- ave(RWJFMeasures_2014$Uninsure_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$PCP_rate <- ave(RWJFMeasures_2014$PCP_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$dentist_rate <- ave(RWJFMeasures_2014$dentist_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$MHP_rate <- ave(RWJFMeasures_2014$MHP_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$hbac_rate <- ave(RWJFMeasures_2014$hbac_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$mammo_percent <- ave(RWJFMeasures_2014$mammo_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$Hsgrad_rate <- ave(RWJFMeasures_2014$Hsgrad_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$college_percent <- ave(RWJFMeasures_2014$college_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$unemploy_rate <- ave(RWJFMeasures_2014$unemploy_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$Childpov_percent <- ave(RWJFMeasures_2014$Childpov_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$singleparent_percent <- ave(RWJFMeasures_2014$singleparent_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$social_assoc_rate <- ave(RWJFMeasures_2014$social_assoc_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$violentcrime_rate <- ave(RWJFMeasures_2014$violentcrime_rate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$airpollution <- ave(RWJFMeasures_2014$airpollution, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$drinkingwater_viol <- ave(RWJFMeasures_2014$drinkingwater_viol, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$severehousing_percent <- ave(RWJFMeasures_2014$severehousing_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$driving_alone_percent <- ave(RWJFMeasures_2014$driving_alone_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$longcommute_percent <- ave(RWJFMeasures_2014$longcommute_percent, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$alc_drivingdeath <- ave(RWJFMeasures_2014$alc_drivingdeath, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$acscrate <- ave(RWJFMeasures_2014$acscrate, RWJFMeasures_2014$State, FUN=scale)
RWJFMeasures_2014$injurydeathrate <- ave(RWJFMeasures_2014$injurydeathrate, RWJFMeasures_2014$State, FUN=scale)
save(RWJFMeasures_2014,file="RWJFMeasures_2014.Rda")Finalize z-scores for performing PCA for Mississippi and West Virginia
Subset health factor variables only, removing county/state info
zscores <- RWJFMeasures_2014[c(1:4,22:50)] Filter to needed states
zMS_WV <- zscores %>% filter(State== "Mississippi" | State == "West Virginia") # two states with declining LE
save(zMS_WV, file="zMS_WV.rda")Load .xlsx of factor names to merge during PCA analysis
factornames <- import("factornames.xlsx")## Warning in strptime(x, format, tz = tz): unknown timezone 'zone/tz/2017c.
## 1.0/zoneinfo/America/New_York'save(factornames, file="factornames.rda")Exploratory Data Analysis
Life Expectancy Decline
In order to get a better understanding of where the most serious decline in life expectancy from 2010 to 2014 is happening in the U.S., we used the below code to generate the list of counties with the largest decrease in life expectancy.
load(file = "finalLE.rda")
load(file = "AllMortality.rda")
load(file = "Predictors_1.rda")#Order of counties with the largest decreased LE (from 2010 - 2014)
ind <- order(ledata$change)
ledata$county[ind] ## [1] Calumet County Bristol City
## [3] Breathitt County Lee County
## [5] Owsley County Lee County
## [7] Leslie County Walker County
## [9] Union County Estill County
## [11] Hardin County Scott County
## [13] Grenada County Anderson County
## [15] Okfuskee County Kiowa County
## [17] Claiborne County Powell County
## [19] Whitley County Citrus County
## [21] Wolfe County East Feliciana Parish
## [23] Jefferson County Cocke County
## [25] Benton County Poinsett County
## [27] Glascock County Panola County
## [29] Wayne County Beckham County
## [31] Pittsburg County Grundy County
## [33] Wheeler County Nicholas County
## [35] Carroll County Neshoba County
## [37] Iron County Union County
## [39] Polk County Potter County
## [41] Dixie County Hawkins County
## [43] Alcorn County Decatur County
## [45] Crenshaw County Walker County
## [47] Gallatin County Magoffin County
## [49] Chester County Wayne County
## [51] Webster County Dunklin County
## [53] Allen County Carroll County
## [55] Macon County Wise County
## [57] McDowell County Barren County
## [59] McDonald County Cherokee County
## [61] Marion County Pulaski County
## [63] Genesee County Tishomingo County
## [65] Perry County Coleman County
## [67] Logan County Mingo County
## [69] Saint Clair County Cross County
## [71] Pike County Scott County
## [73] Caldwell Parish Caddo County
## [75] Henderson County Suwannee County
## [77] Marshall County Graham County
## [79] Cannon County Madison County
## [81] Baltimore City Leake County
## [83] Saint Francois County Bedford County
## [85] Lewis County White County
## [87] Stephens County Jefferson County
## [89] Franklin County McPherson County
## [91] Knott County Rockcastle County
## [93] Cleveland County Tillman County
## [95] Lake County Unicoi County
## [97] Henry County Starke County
## [99] Clinton County Hancock County
## [101] Covington City Owen County
## [103] Clay County Clinton County
## [105] Harlan County Seminole County
## [107] Galax City Marion County
## [109] Lawrence County Jasper County
## [111] Torrance County Houston County
## [113] Scott County Cabell County
## [115] Wise County Butts County
## [117] Polk County Vermilion County
## [119] Cotton County Chester County
## [121] Coffee County Boone County
## [123] Clark County Letcher County
## [125] Macomb County Harrison County
## [127] Bryan County Murray County
## [129] Smith County Stewart County
## [131] Adams County Cochran County
## [133] Gray County Pulaski County
## [135] Wood County Fayette County
## [137] Franklin County Izard County
## [139] Searcy County Sebastian County
## [141] Otero County Murray County
## [143] Fleming County Knox County
## [145] Mason County Robertson County
## [147] Beauregard Parish Jones County
## [149] Washington County Carroll County
## [151] Carter County Dewey County
## [153] Stephens County Pickens County
## [155] Lawrence County McMinn County
## [157] Ochiltree County Mercer County
## [159] Choctaw County Carroll County
## [161] Covington County Independence County
## [163] Franklin Parish Benton County
## [165] Holmes County Stone County
## [167] Roosevelt County San Juan County
## [169] Greer County Lane County
## [171] Knox County Rhea County
## [173] Winkler County Jackson County
## [175] Dougherty County Catahoula Parish
## [177] Berrien County Covington County
## [179] Howell County Benson County
## [181] Grady County Washita County
## [183] Eastland County Giles County
## [185] Franklin County Lamar County
## [187] Toombs County Wayne County
## [189] Johnson County Collingsworth County
## [191] Delta County Madison County
## [193] Trinity County Blount County
## [195] Drew County Jackson County
## [197] Lawrence County White County
## [199] Macon County Shawnee County
## [201] Fulton County Muhlenberg County
## [203] Perry County Claiborne County
## [205] Franklin County Newton County
## [207] Tippah County Morgan County
## [209] Crawford County Vinton County
## [211] Harmon County Hamblen County
## [213] Putnam County Trousdale County
## [215] Armstrong County Freestone County
## [217] Lamar County Loving County
## [219] Tyler County Wilbarger County
## [221] Young County Wyoming County
## [223] Grayson County Morehouse Parish
## [225] Yalobusha County Wayne County
## [227] Butler County Perry County
## [229] Sharp County Grant County
## [231] Itawamba County De Baca County
## [233] Linn County Campbell County
## [235] Obion County Van Buren County
## [237] Dickens County Foard County
## [239] Cleburne County Madison County
## [241] Evans County Bannock County
## [243] Marion County Caldwell County
## [245] Taylor County Sainte Genevieve County
## [247] Nye County Ellis County
## [249] Kingfisher County Hickman County
## [251] Humphreys County Jim Hogg County
## [253] Sabine County Union County
## [255] Hopkins County Hinds County
## [257] Pulaski County Jefferson County
## [259] Lincoln County Robertson County
## [261] Maverick County Greenwood County
## [263] Madison Parish Lawrence County
## [265] Jeff Davis County Madison County
## [267] Gallatin County Vanderburgh County
## [269] Monona County Reno County
## [271] Union County Washington Parish
## [273] Laclede County Ozark County
## [275] Wright County Sierra County
## [277] Hamilton County Grant County
## [279] Rogers County Sequoyah County
## [281] Coke County Lowndes County
## [283] Jasper County Jackson County
## [285] Chautauqua County Floyd County
## [287] Ingham County Randolph County
## [289] Rockingham County Trumbull County
## [291] DeKalb County Montague County
## [293] Clay County Hancock County
## [295] Little River County Hart County
## [297] Jackson County Livingston Parish
## [299] Coahoma County George County
## [301] Jefferson County Eddy County
## [303] Jackson County Klamath County
## [305] Pickett County Bland County
## [307] Berkeley County Lewis County
## [309] Roane County Wilcox County
## [311] Hot Spring County Nevada County
## [313] Colquitt County Gibson County
## [315] Saint Joseph County Cowley County
## [317] Labette County Osage County
## [319] Crittenden County Cumberland County
## [321] Pike County Hillsborough County
## [323] Washington County Marlboro County
## [325] Wayne County Bastrop County
## [327] Nolan County Marshall County
## [329] Marshall County Crawford County
## [331] Anderson County Meigs County
## [333] Colbert County Clarke County
## [335] Montgomery County Red River Parish
## [337] McCurtain County Washington County
## [339] Clay County Clay County
## [341] Saline County Heard County
## [343] Jefferson County Fulton County
## [345] Jennings County Woodson County
## [347] Saint Martin Parish Adair County
## [349] Rowan County Rutherford County
## [351] Summit County Love County
## [353] Ottawa County Texas County
## [355] Jackson County Aransas County
## [357] Hood County Terrell County
## [359] Wayne County Cherokee County
## [361] Costilla County Haralson County
## [363] Upson County Clark County
## [365] Bracken County McCreary County
## [367] Grant Parish Vernon County
## [369] Lyon County Rio Arriba County
## [371] Eddy County Scioto County
## [373] Cherokee County Carson County
## [375] Navarro County Davis County
## [377] Yell County Coffee County
## [379] Lee County Alexander County
## [381] Winnebago County Knox County
## [383] Cerro Gordo County Pointe Coupee Parish
## [385] Vernon Parish Caroline County
## [387] Lawrence County Esmeralda County
## [389] Beaver County Schleicher County
## [391] Cowlitz County Cullman County
## [393] Macon County Chicot County
## [395] Greene County Polk County
## [397] Van Buren County Jenkins County
## [399] Marion County Harvey County
## [401] Lincoln County Livingston County
## [403] Ohio County Webster County
## [405] Saint Landry Parish Winn Parish
## [407] Winston County Buchanan County
## [409] Butler County Madison County
## [411] Reynolds County Gallia County
## [413] Pike County Muskogee County
## [415] Grainger County Maury County
## [417] Donley County Ector County
## [419] Hardeman County Howard County
## [421] Rains County Lynchburg City
## [423] Grady County Pemiscot County
## [425] Colfax County Westmoreland County
## [427] Overton County Dickenson County
## [429] Pike County Clearwater County
## [431] Henry County Saint Clair County
## [433] Sheridan County Davidson County
## [435] Lawrence County Coryell County
## [437] Bibb County Ouachita County
## [439] Boundary County LaSalle County
## [441] Pottawattamie County Garrard County
## [443] Pontotoc County Yazoo County
## [445] Yancey County Jackson County
## [447] Harper County Henderson County
## [449] Garland County Montgomery County
## [451] Bay County Allen County
## [453] Allen County Elliott County
## [455] Larue County Mercer County
## [457] Russell County Avoyelles Parish
## [459] Amite County Jasper County
## [461] Tate County Cibola County
## [463] Valencia County Columbiana County
## [465] Garfield County Washington County
## [467] Clay County Cumberland County
## [469] Brooks County Dimmit County
## [471] Scurry County Beaver County
## [473] Carbon County Escambia County
## [475] Grant County Woodruff County
## [477] Wapello County Menifee County
## [479] Montgomery County Jackson County
## [481] Custer County Erie County
## [483] Bradley County Bosque County
## [485] Hopewell City Hot Springs County
## [487] Ashley County Benton County
## [489] Logan County Randolph County
## [491] Carroll County Towns County
## [493] Ford County Hardin County
## [495] Marshall County Comanche County
## [497] Neosho County Sedgwick County
## [499] Wilson County Calloway County
## [501] Logan County Martin County
## [503] Metcalfe County Huron County
## [505] Attala County Jefferson Davis County
## [507] Smith County Bates County
## [509] Van Wert County Cherokee County
## [511] Coal County Marshall County
## [513] Pawnee County Laurens County
## [515] Crockett County Johnson County
## [517] Sullivan County Haskell County
## [519] Hemphill County Milam County
## [521] Kane County Floyd County
## [523] Radford City Kenosha County
## [525] Jefferson County Rice County
## [527] Hanson County Patrick County
## [529] Washington County Elk County
## [531] Trimble County Andrew County
## [533] Dade County Tipton County
## [535] Hardin County Jefferson County
## [537] Weber County Uinta County
## [539] Randolph County Banks County
## [541] Dade County Early County
## [543] Ware County Cass County
## [545] Clark County LaPorte County
## [547] Orange County Logan County
## [549] Bell County Green County
## [551] Greenup County Owen County
## [553] Calhoun County Clarke County
## [555] Lee County Lincoln County
## [557] Livingston County Webster County
## [559] Renville County Darke County
## [561] Fayette County Giles County
## [563] Caldwell County Barbour County
## [565] Boone County Carroll County
## [567] Union County Mesa County
## [569] Indian River County Wakulla County
## [571] Effingham County Elbert County
## [573] Kankakee County Macoupin County
## [575] Daviess County Floyd County
## [577] Webster County Harper County
## [579] Pratt County Nobles County
## [581] Clay County Benton County
## [583] Lafayette County Linn County
## [585] Sandoval County Haywood County
## [587] Preble County Atoka County
## [589] Pontotoc County Pottawatomie County
## [591] Pushmataha County Wagoner County
## [593] Armstrong County Lee County
## [595] Sequatchie County Warren County
## [597] Liberty County Newton County
## [599] McDowell County Webster County
## [601] Bristol Bay Borough Gulf County
## [603] Randolph County Cass County
## [605] Trigg County Saint Joseph County
## [607] Tunica County Chaves County
## [609] Lawrence County Garvin County
## [611] Josephine County Greene County
## [613] Callahan County Reagan County
## [615] Randolph County Fond du Lac County
## [617] Green Lake County Bledsoe County
## [619] Geneva County Pickens County
## [621] Desha County Clark County
## [623] Jay County Lee County
## [625] Bourbon County Stafford County
## [627] Bath County Harrison County
## [629] Lyon County Monroe County
## [631] Morgan County Pendleton County
## [633] Simpson County Monroe County
## [635] Lowndes County Union County
## [637] Montgomery County Sullivan County
## [639] Caldwell County Ashtabula County
## [641] Comanche County Creek County
## [643] Kay County Le Flore County
## [645] Barnwell County Chesterfield County
## [647] Henry County Camp County
## [649] Hall County Hamilton County
## [651] Hansford County King County
## [653] Panola County Russell County
## [655] Chelan County Grays Harbor County
## [657] Ritchie County Rusk County
## [659] Sawyer County Lamar County
## [661] Montgomery County Hempstead County
## [663] Prairie County Chattooga County
## [665] Wilkinson County Worth County
## [667] DeKalb County Kingman County
## [669] Trego County Iosco County
## [671] Chickasaw County Hancock County
## [673] Ripley County Colfax County
## [675] Noble County Cottle County
## [677] Fisher County Lamb County
## [679] Upshur County Lincoln County
## [681] Putnam County Fulton County
## [683] Howard County Madison County
## [685] Stone County Tift County
## [687] Stark County Elkhart County
## [689] Perry County Monroe County
## [691] Pottawatomie County McLean County
## [693] Caddo Parish Jefferson Davis Parish
## [695] Carroll County Carter County
## [697] Dent County Scott County
## [699] Dodge County Erie County
## [701] Clay County Williams County
## [703] Seneca County Delaware County
## [705] Hughes County Major County
## [707] Marion County Burleson County
## [709] Kenedy County Iron County
## [711] Lee County Natrona County
## [713] Calhoun County Dallas County
## [715] Pope County Volusia County
## [717] Baldwin County Brooks County
## [719] Grundy County Blackford County
## [721] Lawrence County Marion County
## [723] Randolph County Ripley County
## [725] Barber County Breckinridge County
## [727] Christian County Bristol County
## [729] Wilkin County Gasconade County
## [731] Marion County Shannon County
## [733] Strafford County Ocean County
## [735] McKinley County Macon County
## [737] Rolette County Brown County
## [739] Latimer County Curry County
## [741] Jefferson County Wayne County
## [743] McNairy County Kaufman County
## [745] Lavaca County Lee County
## [747] Mitchell County Shelby County
## [749] Sherman County Sevier County
## [751] Braxton County Doddridge County
## [753] Fayette County Upshur County
## [755] Columbia County Appling County
## [757] Greene County Brown County
## [759] Claiborne Parish Brown County
## [761] Robeson County Yadkin County
## [763] Fentress County Hutchinson County
## [765] Jackson County Lynn County
## [767] Palo Pinto County Salt Lake County
## [769] Delta County Moffat County
## [771] Smith County Graves County
## [773] Washington County Ashe County
## [775] Randolph County Brazoria County
## [777] Clay County Lipscomb County
## [779] Eau Claire County DeKalb County
## [781] Marion County Cleveland County
## [783] Faulkner County Lafayette County
## [785] Perry County New London County
## [787] Marion County Lumpkin County
## [789] Rabun County Spalding County
## [791] Twin Falls County Franklin County
## [793] Iroquois County Lee County
## [795] Livingston County Rock Island County
## [797] Warren County Switzerland County
## [799] Washington County Chase County
## [801] Cloud County Carter County
## [803] Marion County Bienville Parish
## [805] LaSalle Parish Lenawee County
## [807] Pike County Caldwell County
## [809] Cooper County Hickory County
## [811] Bernalillo County Luna County
## [813] Socorro County Madison County
## [815] Clark County Clinton County
## [817] Marion County Nowata County
## [819] Meade County Lincoln County
## [821] Dawson County Upton County
## [823] Nicholas County Wirt County
## [825] Buffalo County Juneau County
## [827] Marathon County Lincoln County
## [829] Charlton County Cook County
## [831] Kalawao County Maui County
## [833] De Witt County Ness County
## [835] Rowan County Ouachita Parish
## [837] Jefferson County Cedar County
## [839] Shelby County Mitchell County
## [841] Blaine County Okmulgee County
## [843] Brown County McCulloch County
## [845] Orange County Runnels County
## [847] Wood County Stevens County
## [849] Autauga County Dale County
## [851] Gila County Echols County
## [853] Jerome County Power County
## [855] Delaware County Howard County
## [857] Butler County Decatur County
## [859] Union County Montgomery County
## [861] Casey County Saline County
## [863] Treasure County Onslow County
## [865] Pierce County Muskingum County
## [867] Lycoming County Somerset County
## [869] Oconee County Gibson County
## [871] Roane County Irion County
## [873] Oldham County Buchanan County
## [875] Tuscaloosa County Conway County
## [877] Johnson County Lonoke County
## [879] Miller County Burke County
## [881] Fannin County Irwin County
## [883] Screven County Hamilton County
## [885] Jefferson County Pope County
## [887] Cherokee County Franklin County
## [889] Kearny County Osborne County
## [891] Carroll County Lewis County
## [893] Scott County Concordia Parish
## [895] West Feliciana Parish Kittson County
## [897] Monroe County Perry County
## [899] Clark County Dallas County
## [901] Douglas County Polk County
## [903] Richland County Adams County
## [905] Saline County Grant County
## [907] Bowman County Ransom County
## [909] Harrison County Hocking County
## [911] Montgomery County Perry County
## [913] Richland County Grant County
## [915] Cheatham County Dyer County
## [917] Sevier County Borden County
## [919] Crane County Deaf Smith County
## [921] Duval County Jim Wells County
## [923] Kent County Leon County
## [925] Zavala County Wythe County
## [927] Harrison County Wetzel County
## [929] Sweetwater County Shoshone County
## [931] Rooks County Sabine Parish
## [933] Humphreys County Marion County
## [935] Schuylkill County Carter County
## [937] Calhoun County Moore County
## [939] Navajo County Jackson County
## [941] Edwards County Wilkinson County
## [943] Ramsey County Woodward County
## [945] Andrews County Childress County
## [947] Harrison County Lauderdale County
## [949] Sevier County Jackson County
## [951] Bacon County Lawrence County
## [953] Mason County Montgomery County
## [955] Pulaski County Montgomery County
## [957] Dickinson County Graham County
## [959] Lane County Scott County
## [961] Jessamine County Madison County
## [963] Ascension Parish Iberville Parish
## [965] West Baton Rouge Parish Oxford County
## [967] Harrison County Newton County
## [969] Morrill County Merrimack County
## [971] Bladen County Swain County
## [973] Nelson County Meigs County
## [975] Monroe County Craig County
## [977] Roger Mills County Woods County
## [979] Wasco County Union County
## [981] Davison County Franklin County
## [983] Hardeman County Lauderdale County
## [985] Loudon County Monroe County
## [987] Wilson County Comanche County
## [989] Cooke County DeWitt County
## [991] Hale County Midland County
## [993] Juab County Utah County
## [995] Grayson County Hampton City
## [997] Bradley County Sedgwick County
## [999] Holmes County Terrell County
## [1001] Champaign County Greene County
## [1003] Vermillion County Palo Alto County
## [1005] Plymouth County Jackson Parish
## [1007] West Carroll Parish Mississippi
## [1009] Montgomery County Wayne County
## [1011] Audrain County Barton County
## [1013] Knox County Macon County
## [1015] Furnas County Alamance County
## [1017] Johnston County Brookings County
## [1019] Hamlin County Marshall County
## [1021] Colorado County Grimes County
## [1023] San Augustine County Tom Green County
## [1025] Carbon County Marshall County
## [1027] Cleburne County Craighead County
## [1029] Crittenden County Cheyenne County
## [1031] Ben Hill County Calhoun County
## [1033] Emanuel County Henry County
## [1035] Crawford County Kiowa County
## [1037] Todd County Saint Helena Parish
## [1039] Wicomico County Johnson County
## [1041] Putnam County Orleans County
## [1043] Schenectady County Highland County
## [1045] Lucas County Shelby County
## [1047] Cimarron County Lackawanna County
## [1049] McCook County Crosby County
## [1051] Glasscock County Hunt County
## [1053] Jack County San Jacinto County
## [1055] West Virginia Chambers County
## [1057] Conecuh County Greene County
## [1059] Calhoun County Monroe County
## [1061] Baca County Huerfano County
## [1063] Pueblo County Brevard County
## [1065] Candler County Catoosa County
## [1067] White County Hawaii County
## [1069] Cassia County Douglas County
## [1071] McDonough County Richland County
## [1073] Fulton County Steuben County
## [1075] Boone County Muscatine County
## [1077] Butler County Coffey County
## [1079] Phillips County Republic County
## [1081] Kentucky Anderson County
## [1083] Butler County Rapides Parish
## [1085] Bay County Montcalm County
## [1087] Clay County Greene County
## [1089] Monroe County Pike County
## [1091] Ralls County Lancaster County
## [1093] Lincoln County Merrick County
## [1095] Herkimer County Mercer County
## [1097] Coshocton County Oklahoma
## [1099] Bedford County Beadle County
## [1101] Polk County Weakley County
## [1103] Baylor County Floyd County
## [1105] Hill County Morris County
## [1107] Randall County Red River County
## [1109] Robertson County Ward County
## [1111] Wilson County Utah
## [1113] Alleghany County Danville City
## [1115] Halifax County Martinsville City
## [1117] Pittsylvania County Roanoke County
## [1119] Jackson County Pleasants County
## [1121] Jackson County Walworth County
## [1123] Arkansas Huntington County
## [1125] Morton County Washburn County
## [1127] Humboldt County San Joaquin County
## [1129] Hall County Wheeler County
## [1131] Clay County Decatur County
## [1133] Adams County Sac County
## [1135] Hamilton County Saint Charles Parish
## [1137] Gentry County Ray County
## [1139] Clay County Wood County
## [1141] Allendale County Falls County
## [1143] Sterling County Coffee County
## [1145] Coosa County Elmore County
## [1147] Baxter County Clark County
## [1149] Kern County Oglethorpe County
## [1151] Honolulu County Pike County
## [1153] Franklin County Marshall County
## [1155] Porter County Vigo County
## [1157] Allamakee County Poweshiek County
## [1159] Tama County Ellis County
## [1161] Greeley County Sumner County
## [1163] Wichita County Meade County
## [1165] Ogemaw County Otter Tail County
## [1167] Cole County Miller County
## [1169] New Madrid County Oregon County
## [1171] Burt County Madison County
## [1173] Alleghany County Guilford County
## [1175] Walsh County Marion County
## [1177] Austin County Burnet County
## [1179] Kerr County Limestone County
## [1181] Motley County Gilmer County
## [1183] Burnett County Langlade County
## [1185] Big Horn County Etowah County
## [1187] Phillips County Washington County
## [1189] Rio Grande County Floyd County
## [1191] Stewart County Benewah County
## [1193] Bonneville County Washington County
## [1195] Massac County Saint Clair County
## [1197] Indiana Crawford County
## [1199] Buchanan County Wayne County
## [1201] Gove County Calcasieu Parish
## [1203] Hillsdale County Kent County
## [1205] Osceola County Itasca County
## [1207] Martin County Lawrence County
## [1209] Osage County Fillmore County
## [1211] Curry County Allegany County
## [1213] Hoke County Butler County
## [1215] Luzerne County Kingsbury County
## [1217] Roberts County Dickson County
## [1219] Haywood County Bailey County
## [1221] Cherokee County Dallam County
## [1223] Ellis County Hockley County
## [1225] Pecos County Roberts County
## [1227] Rusk County Petersburg City
## [1229] Marshall County Preston County
## [1231] Grant County Jefferson County
## [1233] Columbia County Kiowa County
## [1235] Levy County Hawaii
## [1237] Johnson County Piatt County
## [1239] Tipton County Union County
## [1241] Ottawa County Cameron Parish
## [1243] Berkshire County Leflore County
## [1245] Camden County Phelps County
## [1247] Johnson County Lander County
## [1249] Stanly County Wilkes County
## [1251] Adair County Washington County
## [1253] Sumter County Piute County
## [1255] Dane County Washington County
## [1257] Crawford County Scott County
## [1259] Bent County Clinch County
## [1261] Jones County Macon County
## [1263] Peach County Morgan County
## [1265] Dubois County Noble County
## [1267] Appanoose County Carroll County
## [1269] Cass County Page County
## [1271] Edwards County Pawnee County
## [1273] Adair County Carlisle County
## [1275] Lafourche Parish Saint Tammany Parish
## [1277] Webster Parish Ottawa County
## [1279] Sanilac County Copiah County
## [1281] Quitman County Chariton County
## [1283] Daviess County Howard County
## [1285] Lewis County Flathead County
## [1287] Musselshell County Douglas County
## [1289] Hitchcock County Kearney County
## [1291] Nuckolls County Lincoln County
## [1293] Mineral County Lea County
## [1295] Mora County Quay County
## [1297] Roosevelt County San Miguel County
## [1299] Union County Surry County
## [1301] Towner County Allen County
## [1303] Hardin County McClain County
## [1305] McIntosh County Oklahoma County
## [1307] Yamhill County Spartanburg County
## [1309] Moore County Angelina County
## [1311] El Paso County Fannin County
## [1313] Garza County Houston County
## [1315] Jones County Lampasas County
## [1317] Nueces County Refugio County
## [1319] Shackelford County Uvalde County
## [1321] Walker County Augusta County
## [1323] Staunton City Waynesboro City
## [1325] Okanogan County Ohio County
## [1327] Chippewa County Polk County
## [1329] Sauk County Putnam County
## [1331] Miller County Seminole County
## [1333] White County Woodford County
## [1335] Adair County Guthrie County
## [1337] Louisa County Clay County
## [1339] Finney County Haskell County
## [1341] Lyon County Boyle County
## [1343] Iberia Parish Vermilion Parish
## [1345] Worcester County Roscommon County
## [1347] Warren County Lincoln County
## [1349] Chase County New Mexico
## [1351] Susquehanna County York County
## [1353] Gaines County Real County
## [1355] Stonewall County Nottoway County
## [1357] Shenandoah County Asotin County
## [1359] Thurston County Ashland County
## [1361] Marengo County Modoc County
## [1363] El Paso County Hartford County
## [1365] Gordon County Boone County
## [1367] Wabash County Humboldt County
## [1369] O'Brien County Polk County
## [1371] Stevens County Hancock County
## [1373] Franklin County Freeborn County
## [1375] Stone County Sanders County
## [1377] Cape May County Logan County
## [1379] Defiance County Huron County
## [1381] Williams County Lancaster County
## [1383] Brule County Codington County
## [1385] Tennessee Hamilton County
## [1387] Briscoe County Lubbock County
## [1389] Martin County King William County
## [1391] Marion County Newton County
## [1393] Mineral County New Haven County
## [1395] Liberty County Morgan County
## [1397] Christian County Tazewell County
## [1399] Newton County Floyd County
## [1401] Henry County Kansas
## [1403] Geary County Spencer County
## [1405] Frederick County Clare County
## [1407] Missaukee County Beltrami County
## [1409] Cass County Rock County
## [1411] Schuyler County Otoe County
## [1413] Thayer County Guadalupe County
## [1415] Otero County Caswell County
## [1417] Logan County Lehigh County
## [1419] Day County Turner County
## [1421] Fayette County Frio County
## [1423] Swisher County Titus County
## [1425] Zapata County Emery County
## [1427] Pacific County Mason County
## [1429] Tyler County Columbia County
## [1431] Tallapoosa County Yuba County
## [1433] Berrien County Wayne County
## [1435] Audubon County Mahaska County
## [1437] Rush County Edmonson County
## [1439] Fayette County Hardin County
## [1441] Acadia Parish Chippewa County
## [1443] Adams County Walthall County
## [1445] Big Horn County Alexander County
## [1447] Sullivan County Jasper County
## [1449] Washington County Warren County
## [1451] Calhoun County Decatur County
## [1453] Howard County Linn County
## [1455] Wibaux County Sutton County
## [1457] Chilton County Southeast Fairbanks Census Area
## [1459] Clarke County Crawford County
## [1461] Pierce County Sumter County
## [1463] Gooding County Payette County
## [1465] Adams County Saline County
## [1467] Morgan County Ohio County
## [1469] White County Harrison County
## [1471] Winnebago County Sherman County
## [1473] Ballard County Laurel County
## [1475] Warren County East Baton Rouge Parish
## [1477] Saint John the Baptist Parish Cecil County
## [1479] Marquette County Carlton County
## [1481] Isanti County Pipestone County
## [1483] Red Lake County Redwood County
## [1485] Prentiss County Sunflower County
## [1487] Taney County Warren County
## [1489] Webster County York County
## [1491] Churchill County New Hampshire
## [1493] Onondaga County Davie County
## [1495] Gaston County Barnes County
## [1497] Stark County Champaign County
## [1499] Canadian County Mayes County
## [1501] Payne County Union County
## [1503] Wyoming County Sanborn County
## [1505] Blount County Morgan County
## [1507] Erath County Live Oak County
## [1509] Medina County Smith County
## [1511] Throckmorton County Wayne County
## [1513] Colonial Heights City Page County
## [1515] Clark County Monroe County
## [1517] Price County Yavapai County
## [1519] Saint Francis County Cumberland County
## [1521] Sangamon County Williamson County
## [1523] Henry County Oscoda County
## [1525] Shiawassee County Stearns County
## [1527] Cape Girardeau County Maries County
## [1529] Moniteau County Perry County
## [1531] Stoddard County Nebraska
## [1533] Cumberland County Lincoln County
## [1535] Stutsman County Coos County
## [1537] Malheur County Bristol County
## [1539] Lake County Montgomery County
## [1541] Somervell County Terry County
## [1543] Racine County Pike County
## [1545] Conejos County Logan County
## [1547] Brantley County Turner County
## [1549] Ada County Henderson County
## [1551] Putnam County Scott County
## [1553] Brown County Putnam County
## [1555] Emmet County Hamilton County
## [1557] Jackson County Lincoln County
## [1559] Campbell County Jefferson County
## [1561] East Carroll Parish Terrebonne Parish
## [1563] Essex County Saint Clair County
## [1565] Washington County Franklin County
## [1567] Red Willow County Richardson County
## [1569] Hidalgo County Steuben County
## [1571] Person County Adams County
## [1573] Ross County Campbell County
## [1575] Stanley County Atascosa County
## [1577] Chambers County Crockett County
## [1579] Waller County Yoakum County
## [1581] Box Elder County Franklin County
## [1583] Prince Edward County Smyth County
## [1585] Mason County Sheridan County
## [1587] Tuolumne County Dolores County
## [1589] Litchfield County Franklin County
## [1591] Union County Clearwater County
## [1593] Moultrie County Pike County
## [1595] Whitley County Cherokee County
## [1597] Greene County Linn County
## [1599] Shelby County Atchison County
## [1601] Decatur County Morris County
## [1603] Rawlins County Wallace County
## [1605] Anne Arundel County Lyon County
## [1607] Bolivar County Tallahatchie County
## [1609] Barry County Nodaway County
## [1611] Hill County Prairie County
## [1613] Knox County Sarpy County
## [1615] Salem County Montgomery County
## [1617] Niagara County Avery County
## [1619] Harnett County Adams County
## [1621] Oliver County Greene County
## [1623] Wyandot County Beaver County
## [1625] Northampton County Colleton County
## [1627] Darlington County Horry County
## [1629] Fall River County Madison County
## [1631] Victoria County Kitsap County
## [1633] Walla Walla County Barron County
## [1635] Portage County Montgomery County
## [1637] Warren County Bear Lake County
## [1639] Caribou County Clay County
## [1641] Mitchell County Lincoln Parish
## [1643] Pearl River County Dixon County
## [1645] Nemaha County Eureka County
## [1647] Humboldt County Yates County
## [1649] Stokes County Haakon County
## [1651] Emporia City Greensville County
## [1653] Madison County Limestone County
## [1655] Lee County Mitchell County
## [1657] Johnson County Hardin County
## [1659] Worth County Box Butte County
## [1661] Traill County Abbeville County
## [1663] McLennan County Tooele County
## [1665] Talladega County Anchorage Municipality
## [1667] Sierra County Crowley County
## [1669] Rio Blanco County Calhoun County
## [1671] Taylor County Habersham County
## [1673] Putnam County Franklin County
## [1675] Gem County Kootenai County
## [1677] Crawford County Effingham County
## [1679] Hancock County Jasper County
## [1681] Benton County Fayette County
## [1683] Hendricks County Parke County
## [1685] Wells County Iowa
## [1687] Cedar County Dubuque County
## [1689] Fayette County Barton County
## [1691] Doniphan County Douglas County
## [1693] Russell County Thomas County
## [1695] Boyd County Hickman County
## [1697] Woodford County Washington County
## [1699] Muskegon County Wexford County
## [1701] Kandiyohi County Morrison County
## [1703] Murray County Polk County
## [1705] Choctaw County Jackson County
## [1707] Texas County Thurston County
## [1709] Washington County Monroe County
## [1711] Iredell County Ohio
## [1713] Ashland County Columbia County
## [1715] Moody County Perkins County
## [1717] Archer County Castro County
## [1719] Franklin County Gregg County
## [1721] Grant County Greenbrier County
## [1723] Summers County Campbell County
## [1725] Monroe County Gilchrist County
## [1727] Bryan County Hart County
## [1729] Twiggs County Edgar County
## [1731] Pulaski County Sullivan County
## [1733] Jackson County Louisiana
## [1735] Queen Anne's County Franklin County
## [1737] Norfolk County Tuscola County
## [1739] Mahnomen County Wadena County
## [1741] Sharkey County Bollinger County
## [1743] Scotland County Rosebud County
## [1745] Pawnee County Phelps County
## [1747] Anson County Burke County
## [1749] Greene County Morrow County
## [1751] Wheeler County McKean County
## [1753] Warren County Saluda County
## [1755] Clark County Jerauld County
## [1757] Goliad County Starr County
## [1759] Van Zandt County Cache County
## [1761] Alabama Alamosa County
## [1763] Treutlen County Camas County
## [1765] Ogle County Jasper County
## [1767] Davis County Iowa County
## [1769] Van Buren County Saline County
## [1771] Henderson County Washington County
## [1773] Bossier Parish Gladwin County
## [1775] Renville County Worth County
## [1777] Missoula County Clinton County
## [1779] Henderson County Grant County
## [1781] Tulsa County Aiken County
## [1783] Fairfield County Hutchinson County
## [1785] Walworth County Menard County
## [1787] Nacogdoches County Taylor County
## [1789] Willacy County Charlotte County
## [1791] Frederick County Clallam County
## [1793] Pocahontas County Brown County
## [1795] Lincoln County Marinette County
## [1797] Barbour County Hale County
## [1799] Arkansas County Dallas County
## [1801] Tehama County Okaloosa County
## [1803] Bulloch County Chattahoochee County
## [1805] Johnson County Meriwether County
## [1807] Newton County Perry County
## [1809] Adams County Bartholomew County
## [1811] Boone County Fountain County
## [1813] Miami County Black Hawk County
## [1815] Hancock County Ida County
## [1817] Lucas County Ringgold County
## [1819] Taylor County Washington County
## [1821] Woodbury County Jefferson County
## [1823] Seward County Stanton County
## [1825] Bourbon County De Soto Parish
## [1827] Tangipahoa Parish Allegany County
## [1829] Dorchester County Michigan
## [1831] Kalkaska County Saginaw County
## [1833] Benton County Meeker County
## [1835] Pine County Sibley County
## [1837] DeSoto County Toole County
## [1839] Butler County Custer County
## [1841] Dawson County Gosper County
## [1843] Broome County Chemung County
## [1845] Tioga County Martin County
## [1847] Perquimans County Foster County
## [1849] Guernsey County Alfalfa County
## [1851] Benton County Jackson County
## [1853] Polk County Clarion County
## [1855] Dillon County Sumner County
## [1857] Culberson County Johnson County
## [1859] Kleberg County Knox County
## [1861] Mills County Garfield County
## [1863] Chesterfield County Northampton County
## [1865] Kanawha County Fremont County
## [1867] Las Animas County Tattnall County
## [1869] Oktibbeha County Currituck County
## [1871] Gilmer County Idaho
## [1873] Oneida County Logan County
## [1875] Lake County Chippewa County
## [1877] Lake County Lincoln County
## [1879] Fulton County Griggs County
## [1881] Ward County Erie County
## [1883] Mahoning County Parmer County
## [1885] San Patricio County Converse County
## [1887] Winston County Matanuska-Susitna Borough
## [1889] Apache County Shasta County
## [1891] Fremont County Montrose County
## [1893] Laurens County Minidoka County
## [1895] Menard County Rush County
## [1897] Anderson County Dickinson County
## [1899] Oakland County Grant County
## [1901] Mille Lacs County Ramsey County
## [1903] Atchison County Carson City
## [1905] Coos County Seneca County
## [1907] Forsyth County Kidder County
## [1909] Fulton County Mercer County
## [1911] Allegheny County Bradford County
## [1913] Hampton County McCormick County
## [1915] York County Miner County
## [1917] Kimble County Bath County
## [1919] Spokane County Crawford County
## [1921] Monroe County Washakie County
## [1923] Fairbanks North Star Borough DeKalb County
## [1925] Schuyler County Clinton County
## [1927] Grant County Crawford County
## [1929] Jones County Sheridan County
## [1931] Tensas Parish Allegan County
## [1933] Emmet County Big Stone County
## [1935] Yellow Medicine County Kemper County
## [1937] Noxubee County Chouteau County
## [1939] McCone County Gage County
## [1941] Hamilton County Harlan County
## [1943] Perkins County Rock County
## [1945] Burlington County Catawba County
## [1947] Craven County Wayne County
## [1949] Marion County Lancaster County
## [1951] Jefferson County Cameron County
## [1953] Reeves County Portsmouth City
## [1955] Adams County Lewis County
## [1957] Waushara County Choctaw County
## [1959] Connecticut Flagler County
## [1961] Pickens County Schley County
## [1963] Clinton County Mercer County
## [1965] Clay County Osceola County
## [1967] York County Lincoln County
## [1969] Steele County Christian County
## [1971] Grundy County Mercer County
## [1973] Mineral County Brown County
## [1975] Dundy County Seward County
## [1977] New Hanover County North Dakota
## [1979] Haskell County Brown County
## [1981] Bandera County Hopkins County
## [1983] Rockwall County Dinwiddie County
## [1985] Orange County Houston County
## [1987] Plumas County Gadsden County
## [1989] Jefferson County Barrow County
## [1991] Lincoln County Wilcox County
## [1993] Canyon County Lemhi County
## [1995] Madison County Martin County
## [1997] Tippecanoe County Dickinson County
## [1999] Grundy County Story County
## [2001] Winneshiek County Ellsworth County
## [2003] Gray County Wabaunsee County
## [2005] Lincoln County Cheboygan County
## [2007] Clinton County Eaton County
## [2009] Ionia County Mason County
## [2011] Midland County Crow Wing County
## [2013] Simpson County
## [2015] Mississippi County Dawson County
## [2017] Phillips County Ravalli County
## [2019] Kimball County Logan County
## [2021] Pierce County Lincoln County
## [2023] Halifax County Pamlico County
## [2025] Golden Valley County Hettinger County
## [2027] Holmes County Stark County
## [2029] Clatsop County Wallowa County
## [2031] Franklin County Perry County
## [2033] Potter County Kershaw County
## [2035] Deuel County Potter County
## [2037] Bee County Hartley County
## [2039] Millard County Appomattox County
## [2041] Calhoun County Montmorency County
## [2043] Dickey County Wilkes County
## [2045] Shelby County McCracken County
## [2047] Evangeline Parish Aroostook County
## [2049] Benzie County Scott County
## [2051] Fallon County Douglas County
## [2053] White Pine County Sampson County
## [2055] Wells County Hancock County
## [2057] Lyman County Wichita County
## [2059] Culpeper County Ferry County
## [2061] Vernon County Glenn County
## [2063] Jersey County Jo Daviess County
## [2065] Nemaha County Shelby County
## [2067] Saint Bernard Parish Piscataquis County
## [2069] Carroll County Isabella County
## [2071] Schoolcraft County Cottonwood County
## [2073] Pennington County Clay County
## [2075] Holt County Park County
## [2077] Cass County Thomas County
## [2079] Oneida County Warren County
## [2081] Grand Forks County Morton County
## [2083] Baker County Hood River County
## [2085] Tillamook County Clinton County
## [2087] Montour County Bexar County
## [2089] Brazos County Fayette County
## [2091] Grayson County Whitman County
## [2093] Morgan County Vilas County
## [2095] Weston County Montezuma County
## [2097] Windham County Taylor County
## [2099] Elmore County Idaho County
## [2101] Knox County Carroll County
## [2103] Buena Vista County Wright County
## [2105] Meade County Miami County
## [2107] Norton County Riley County
## [2109] Franklin County Nelson County
## [2111] Oldham County Union Parish
## [2113] Sagadahoc County Hampden County
## [2115] Crawford County Houghton County
## [2117] Iron County Blue Earth County
## [2119] Mower County Norman County
## [2121] Madison County DeKalb County
## [2123] Polk County Hertford County
## [2125] Scotland County Morgan County
## [2127] Crawford County Calhoun County
## [2129] Richland County Clay County
## [2131] McPherson County Bowie County
## [2133] Gonzales County Wharton County
## [2135] Bennington County Rutland County
## [2137] Amelia County Tazewell County
## [2139] Westmoreland County Douglas County
## [2141] Pend Oreille County Manitowoc County
## [2143] Calaveras County Fresno County
## [2145] Bingham County Lincoln County
## [2147] Fayette County Dearborn County
## [2149] Des Moines County Madison County
## [2151] Mills County Grant County
## [2153] Hancock County Dodge County
## [2155] Fergus County Powell County
## [2157] Scotts Bluff County Billings County
## [2159] Athens County Medina County
## [2161] Providence County Cass County
## [2163] Kinney County Pendleton County
## [2165] Tucker County Wyoming
## [2167] Lafayette County Sheridan County
## [2169] Divide County Lake County
## [2171] Merced County Prowers County
## [2173] Yuma County Okeechobee County
## [2175] Pasco County Polk County
## [2177] Walton County Washington County
## [2179] Clark County Lewis County
## [2181] Jackson County LaGrange County
## [2183] Benton County Warren County
## [2185] Jewell County Marshall County
## [2187] Washington County Richland Parish
## [2189] Gogebic County Manistee County
## [2191] Traverse County Winona County
## [2193] Callaway County Golden Valley County
## [2195] Yellowstone County Cuming County
## [2197] Valley County Genesee County
## [2199] Oswego County Franklin County
## [2201] Vance County Bottineau County
## [2203] Portage County Tuscarawas County
## [2205] Oregon Butler County
## [2207] Columbia County Aurora County
## [2209] Hughes County Edwards County
## [2211] Guadalupe County McMullen County
## [2213] Sanpete County Washington County
## [2215] Campbell County Essex County
## [2217] Hardy County Forest County
## [2219] Kewaunee County Siskiyou County
## [2221] Kit Carson County Dodge County
## [2223] Illinois Washington County
## [2225] Kosciusko County Monroe County
## [2227] Calhoun County Keokuk County
## [2229] Kossuth County Lyon County
## [2231] Saint Mary Parish Barry County
## [2233] Fillmore County Todd County
## [2235] Issaquena County Pettis County
## [2237] Custer County Garfield County
## [2239] Washington County Auglaize County
## [2241] Union County Washington County
## [2243] Indiana County Karnes County
## [2245] Llano County Daggett County
## [2247] Richland County Platte County
## [2249] Henry County Phillips County
## [2251] Baker County Hamilton County
## [2253] Lake County Forsyth County
## [2255] Quitman County Peoria County
## [2257] Whiteside County Fremont County
## [2259] Pocahontas County Bullitt County
## [2261] Barnstable County Lapeer County
## [2263] Livingston County Oceana County
## [2265] Ontonagon County Jackson County
## [2267] Rice County Meagher County
## [2269] Nance County Platte County
## [2271] Stanton County Elko County
## [2273] Grafton County Sullivan County
## [2275] Taos County Nash County
## [2277] McHenry County McLean County
## [2279] Carbon County Yankton County
## [2281] Concho County Middlesex County
## [2283] Norton City Brooke County
## [2285] Wisconsin Marquette County
## [2287] Waupaca County Escambia County
## [2289] Bartow County Paulding County
## [2291] Carroll County Harrison County
## [2293] Franklin County Jefferson County
## [2295] Mitchell County Wyandotte County
## [2297] Kenton County Allen Parish
## [2299] Maine Arenac County
## [2301] Olmsted County Greene County
## [2303] Pondera County Loup County
## [2305] Edgecombe County Northampton County
## [2307] Cass County Miami County
## [2309] Paulding County Warren County
## [2311] Douglas County Beaufort County
## [2313] Matagorda County Grand County
## [2315] Accomack County Craig County
## [2317] Gloucester County Greene County
## [2319] Henrico County Taylor County
## [2321] Clark County La Crosse County
## [2323] Saint Croix County Teller County
## [2325] Camden County Bond County
## [2327] Wabash County Cumberland County
## [2329] Buffalo County Garden County
## [2331] Cuyahoga County Gregory County
## [2333] Todd County Harrisonburg City
## [2335] Rockingham County Wahkiakum County
## [2337] Johnson County Clarke County
## [2339] Jasper County Licking County
## [2341] Salem City Alaska
## [2343] Pulaski County Del Norte County
## [2345] Adams County Arapahoe County
## [2347] Boulder County Broomfield County
## [2349] Denver County Jefferson County
## [2351] Weld County Bleckley County
## [2353] Crisp County Talbot County
## [2355] Bonner County Daviess County
## [2357] Saint Mary's County Massachusetts
## [2359] Hampshire County Wayne County
## [2361] Kanabec County Watonwan County
## [2363] Forrest County Montana
## [2365] Daniels County Judith Basin County
## [2367] Teton County Banner County
## [2369] Deuel County Hayes County
## [2371] Rockingham County Chautauqua County
## [2373] Gates County Jones County
## [2375] Watauga County Sandusky County
## [2377] Pennsylvania Dewey County
## [2379] Galveston County Stephens County
## [2381] Richmond County Whatcom County
## [2383] Oneida County Rock County
## [2385] Goshen County Mobile County
## [2387] La Plata County Hernando County
## [2389] Nassau County Adams County
## [2391] Bureau County Will County
## [2393] Van Buren County Holt County
## [2395] Howard County Sioux County
## [2397] Storey County Harding County
## [2399] Pender County Polk County
## [2401] Montgomery County Williamsburg County
## [2403] Tripp County Uintah County
## [2405] Franklin City Mathews County
## [2407] Southampton County Washington
## [2409] Grant County Crook County
## [2411] Henry County Owyhee County
## [2413] Clinton County Penobscot County
## [2415] Alcona County Faribault County
## [2417] Cascade County Belknap County
## [2419] Burke County Lake County
## [2421] Lorain County Fulton County
## [2423] Rhode Island Caroline County
## [2425] Norfolk City Monongalia County
## [2427] Raleigh County Douglas County
## [2429] Park County Sumter County
## [2431] Colorado Lafayette County
## [2433] Atkinson County Monroe County
## [2435] Warren County Warrick County
## [2437] Chickasaw County Delaware County
## [2439] Assumption Parish Gratiot County
## [2441] Mecosta County Newaygo County
## [2443] Chisago County Douglas County
## [2445] Houston County Petroleum County
## [2447] Stillwater County Blaine County
## [2449] Dawes County Saunders County
## [2451] Rensselaer County Montgomery County
## [2453] Henry County Gilliam County
## [2455] Adams County Monroe County
## [2457] Venango County Butte County
## [2459] Faulk County Bell County
## [2461] Webb County Orange County
## [2463] Lancaster County Door County
## [2465] Sublette County Lanier County
## [2467] Jefferson County Stephenson County
## [2469] Madison County Posey County
## [2471] Becker County Morrow County
## [2473] Spink County James City County
## [2475] Williamsburg City Shawano County
## [2477] Butte County Grand Traverse County
## [2479] Aitkin County Le Sueur County
## [2481] Wheatland County Green County
## [2483] Valdez-Cordova Census Area Greenlee County
## [2485] Saguache County Kent County
## [2487] Hancock County Webster County
## [2489] Latah County Nez Perce County
## [2491] Brown County Boone County
## [2493] Natchitoches Parish Minnesota
## [2495] Hubbard County Waseca County
## [2497] Lauderdale County Cherry County
## [2499] Cheyenne County Camden County
## [2501] North Carolina Richmond County
## [2503] Emmons County Pickaway County
## [2505] Crook County Forest County
## [2507] Huntingdon County Northumberland County
## [2509] Bamberg County Clarendon County
## [2511] Dallas County Mason County
## [2513] Duchesne County Columbia County
## [2515] Snohomish County Yakima County
## [2517] Lincoln County Archuleta County
## [2519] Lincoln County Marion County
## [2521] Troup County Ford County
## [2523] Hodgeman County Charlevoix County
## [2525] Nicollet County Roseau County
## [2527] Swift County Glacier County
## [2529] Dakota County Keya Paha County
## [2531] Atlantic County Catron County
## [2533] Chenango County Pasquotank County
## [2535] Berks County South Dakota
## [2537] Douglas County Rich County
## [2539] Buckingham County Hanover County
## [2541] Klickitat County Dunn County
## [2543] Trinity County Lake County
## [2545] McLean County Johnson County
## [2547] Androscoggin County Alpena County
## [2549] Pope County Jefferson County
## [2551] Lake County Arthur County
## [2553] Wheeler County Beaufort County
## [2555] Lenoir County McIntosh County
## [2557] Putnam County Elk County
## [2559] Harding County Newport News City
## [2561] York County Pierce County
## [2563] Juneau City and Borough Maricopa County
## [2565] Mississippi County Santa Cruz County
## [2567] Saint Johns County Liberty County
## [2569] Randolph County Walton County
## [2571] Marion County Dallas County
## [2573] Waldo County Cass County
## [2575] Carter County Powder River County
## [2577] Boyd County Cortland County
## [2579] Ontario County Camden County
## [2581] Jackson County Wake County
## [2583] Fairfield County Knox County
## [2585] Lake County Jefferson County
## [2587] Edmunds County Lawrence County
## [2589] Sully County Union County
## [2591] Blanco County Caledonia County
## [2593] Orleans County Botetourt County
## [2595] Clarke County Skamania County
## [2597] Hampshire County Oconto County
## [2599] Harford County Lewis and Clark County
## [2601] Texas Bradford County
## [2603] Plymouth County Saint Louis County
## [2605] Richland County Jones County
## [2607] Jefferson County Jefferson County
## [2609] Alpine County El Dorado County
## [2611] Gilpin County Oconee County
## [2613] Taliaferro County Cook County
## [2615] Kendall County Calvert County
## [2617] Talbot County Branch County
## [2619] McLeod County Wright County
## [2621] Lamar County Boone County
## [2623] Passaic County Brunswick County
## [2625] Columbus County Pitt County
## [2627] Sheridan County Osage County
## [2629] Mifflin County South Carolina
## [2631] Hidalgo County San Saba County
## [2633] Chesapeake City Cumberland County
## [2635] Lunenburg County Poquoson City
## [2637] Sussex County Lincoln County
## [2639] Albany County Niobrara County
## [2641] Graham County Amador County
## [2643] Nevada County Garfield County
## [2645] Middlesex County Martin County
## [2647] Bibb County Clayton County
## [2649] Johnson County Sioux County
## [2651] Jefferson Parish Kennebec County
## [2653] Otsego County Lake of the Woods County
## [2655] Deer Lodge County Pershing County
## [2657] Bon Homme County Corson County
## [2659] Parker County Isle of Wight County
## [2661] Mecklenburg County Bayfield County
## [2663] Florence County Waukesha County
## [2665] Lowndes County Baraga County
## [2667] Scott County Burleigh County
## [2669] Newport County Greenville County
## [2671] Jackson County Lexington City
## [2673] Trempealeau County Bullock County
## [2675] Bethel Census Area Haines Borough
## [2677] Ketchikan Gateway Borough Petersburg Borough
## [2679] Prince of Wales-Hyder Census Area Wrangell City and Borough
## [2681] Larimer County Fairfield County
## [2683] Clay County McDuffie County
## [2685] Rockdale County Thomas County
## [2687] Whitfield County Monroe County
## [2689] Spencer County Cheyenne County
## [2691] Lac qui Parle County Jackson County
## [2693] Hooker County Keith County
## [2695] Sherman County Wayne County
## [2697] Gloucester County Cayuga County
## [2699] Madison County Schuyler County
## [2701] Sioux County Edgefield County
## [2703] Florence County Newberry County
## [2705] Marshall County Kendall County
## [2707] Windsor County Amherst County
## [2709] Nelson County Prince George County
## [2711] Winchester City Skagit County
## [2713] Winnebago County Custer County
## [2715] Somerset County Kent County
## [2717] Cass County Saint Louis County
## [2719] Broadwater County Montgomery County
## [2721] Iron County Pepin County
## [2723] United States Sacramento County
## [2725] San Juan County Santa Rosa County
## [2727] Clay County DuPage County
## [2729] McHenry County Garrett County
## [2731] Platte County Valley County
## [2733] Frontier County Cheshire County
## [2735] Somerset County Franklin County
## [2737] Wayne County Wyoming County
## [2739] Carteret County Madison County
## [2741] Harney County Sherman County
## [2743] Mercer County Tioga County
## [2745] Shelby County Highland County
## [2747] Virginia Beach City King County
## [2749] Pierce County San Juan County
## [2751] Washington County Mariposa County
## [2753] Kauai County Bremer County
## [2755] Saint James Parish Franklin County
## [2757] Greene County Otsego County
## [2759] Chowan County Dare County
## [2761] Moore County LaMoure County
## [2763] Steele County Logan County
## [2765] Greene County Morgan County
## [2767] Santa Barbara County Delaware County
## [2769] Mellette County Madison County
## [2771] Highlands County Houston County
## [2773] Richmond County Boone County
## [2775] Benton County Baldwin County
## [2777] Denali Borough Yukon-Koyukuk Census Area
## [2779] Arizona Park County
## [2781] Hardee County Dooly County
## [2783] Delta County Menominee County
## [2785] Presque Isle County Wabasha County
## [2787] Washington County Union County
## [2789] Los Alamos County Orange County
## [2791] Ulster County Granville County
## [2793] Pembina County Umatilla County
## [2795] Kent County Washington County
## [2797] Jeff Davis County Williamson County
## [2799] Vermont Garfield County
## [2801] Morgan County Santa Cruz County
## [2803] Butte County Napa County
## [2805] Goodhue County Lake County
## [2807] Sweet Grass County Hall County
## [2809] Bertie County Transylvania County
## [2811] Clackamas County Grant County
## [2813] Multnomah County Fayette County
## [2815] Charles Mix County Harris County
## [2817] Franklin County Kenai Peninsula Borough
## [2819] Coconino County Mendocino County
## [2821] Morgan County Washington County
## [2823] Leavenworth County Knox County
## [2825] Silver Bow County Cambria County
## [2827] Dorchester County Orangeburg County
## [2829] Summit County Lamoille County
## [2831] Washington County Franklin County
## [2833] Island County Georgia
## [2835] Telfair County Plaquemines Parish
## [2837] Middlesex County Hennepin County
## [2839] Blaine County Jefferson County
## [2841] Antelope County Morris County
## [2843] Santa Fe County Dutchess County
## [2845] Suffolk County Buncombe County
## [2847] Dunn County Slope County
## [2849] Chester County Clearfield County
## [2851] Greenwood County Buffalo County
## [2853] Wasatch County Goochland County
## [2855] Sheboygan County Sussex County
## [2857] Harris County Westchester County
## [2859] Hand County Virginia
## [2861] Long County Alger County
## [2863] Saint Charles County Montgomery County
## [2865] Stanislaus County Elbert County
## [2867] Jackson County San Miguel County
## [2869] Seminole County Dawson County
## [2871] Douglas County Boise County
## [2873] Coles County Koochiching County
## [2875] McPherson County Schoharie County
## [2877] Warren County Lincoln County
## [2879] Hyde County Lincoln County
## [2881] Val Verde County Fauquier County
## [2883] Fluvanna County Washington County
## [2885] Iowa County Cochise County
## [2887] Ventura County Ouray County
## [2889] Gwinnett County Somerset County
## [2891] Carver County Carroll County
## [2893] Bergen County Columbia County
## [2895] Hamilton County Franklin County
## [2897] Anderson County La Salle County
## [2899] Presidio County Rappahannock County
## [2901] Milwaukee County La Paz County
## [2903] Yuma County Henry County
## [2905] Gallatin County Sullivan County
## [2907] Cameron County Cumberland County
## [2909] Snyder County Minnehaha County
## [2911] Worcester County Stevens County
## [2913] Liberty County Dona Ana County
## [2915] Tompkins County Duplin County
## [2917] Lee County Ottawa County
## [2919] Dauphin County Brewster County
## [2921] Buena Vista City King George County
## [2923] King and Queen County Rockbridge County
## [2925] Lafayette County Custer County
## [2927] Dakota County New Jersey
## [2929] Clermont County Pima County
## [2931] Mono County Cedar County
## [2933] Grand Isle County Inyo County
## [2935] Lassen County Saint Lucie County
## [2937] Coweta County Teton County
## [2939] Kane County Scott County
## [2941] Anoka County Beaverhead County
## [2943] Warren County Lewis County
## [2945] Geauga County Blair County
## [2947] Juniata County Davidson County
## [2949] Shelby County Hillsborough County
## [2951] Leon County Blaine County
## [2953] Fremont County Shelby County
## [2955] Antrim County Kalamazoo County
## [2957] Mackinac County Cabarrus County
## [2959] Rutherford County San Juan County
## [2961] Northumberland County Mineral County
## [2963] Luce County Greeley County
## [2965] Wayne County Fredericksburg City
## [2967] Louisa County Spotsylvania County
## [2969] Kusilvak Census Area Glynn County
## [2971] Washtenaw County Garfield County
## [2973] Monmouth County Sussex County
## [2975] Cattaraugus County Saratoga County
## [2977] Mecklenburg County Noble County
## [2979] Bucks County Windham County
## [2981] Maryland Leelanau County
## [2983] Marshall County Laramie County
## [2985] Gunnison County Alachua County
## [2987] Cook County Nevada
## [2989] New York Cumberland County
## [2991] Washington County Sargent County
## [2993] Bennett County Gillespie County
## [2995] Hudspeth County Wood County
## [2997] Clear Creek County Charlotte County
## [2999] Greene County Muscogee County
## [3001] Pulaski County Lafayette Parish
## [3003] Carbon County Granite County
## [3005] Cavalier County Addison County
## [3007] Chittenden County Bedford County
## [3009] Charles City County Grand County
## [3011] Washoe County Cleveland County
## [3013] New Kent County Jefferson County
## [3015] Taylor County Albany County
## [3017] Suffolk City San Benito County
## [3019] Delaware Chatham County
## [3021] Cobb County Essex County
## [3023] Hyde County Mountrail County
## [3025] Delaware County Philadelphia County
## [3027] Georgetown County Powhatan County
## [3029] Kittitas County Comal County
## [3031] Sarasota County Baker County
## [3033] Fayette County Jefferson County
## [3035] Wilson County Albemarle County
## [3037] Charlottesville City Outagamie County
## [3039] Palm Beach County McIntosh County
## [3041] Dukes County Middlesex County
## [3043] Union County Denton County
## [3045] Essex County Pennington County
## [3047] Placer County Queens County
## [3049] Delaware County Orange County
## [3051] San Bernardino County Hinsdale County
## [3053] Osceola County Pinellas County
## [3055] Hamilton County Hancock County
## [3057] Madison County Clark County
## [3059] Mercer County Lebanon County
## [3061] Pike County Custer County
## [3063] Brunswick County Surry County
## [3065] Mohave County Sutter County
## [3067] Duval County Nantucket County
## [3069] Grant County Routt County
## [3071] Sherburne County Tyrrell County
## [3073] Berkeley County Ziebach County
## [3075] Tarrant County Contra Costa County
## [3077] Kings County Madera County
## [3079] Santa Clara County Tulare County
## [3081] Florida Livingston County
## [3083] Belmont County Arlington County
## [3085] Charles County Nassau County
## [3087] Douglas County Tolland County
## [3089] Manatee County Columbia County
## [3091] Valley County Richmond County
## [3093] Sitka City and Borough Riverside County
## [3095] Chaffee County Hoonah-Angoon Census Area
## [3097] Skagway Municipality Yakutat City and Borough
## [3099] Hays County Lake County
## [3101] California Saint Lawrence County
## [3103] Dillingham Census Area Lake and Peninsula Borough
## [3105] Yolo County Saint Louis City
## [3107] Union County Collin County
## [3109] Nome Census Area Roanoke City
## [3111] Stafford County Ozaukee County
## [3113] Northwest Arctic Borough Imperial County
## [3115] Solano County Summit County
## [3117] New Castle County Rankin County
## [3119] Deschutes County Lexington County
## [3121] Williamson County Fairfax City
## [3123] Fairfax County Monterey County
## [3125] Teton County Hunterdon County
## [3127] Dodge County Pitkin County
## [3129] Orange County Kodiak Island Borough
## [3131] Baltimore County Keweenaw County
## [3133] Putnam County McKenzie County
## [3135] Alameda County Colusa County
## [3137] Glades County Monroe County
## [3139] San Luis Obispo County Manassas City
## [3141] Manassas Park City Prince William County
## [3143] Russell County Oglala Lakota County
## [3145] Travis County Loudoun County
## [3147] San Diego County Falls Church City
## [3149] DeSoto County Clayton County
## [3151] Orleans Parish Hendry County
## [3153] Calhoun County Eagle County
## [3155] Menominee County DeKalb County
## [3157] Pinal County Howard County
## [3159] Chatham County Centre County
## [3161] Jasper County Los Angeles County
## [3163] Johnston County Bronx County
## [3165] Charleston County Fort Bend County
## [3167] Fulton County North Slope Borough
## [3169] District of Columbia District of Columbia
## [3171] New York County Lee County
## [3173] Suffolk County Marin County
## [3175] Hudson County Alexandria City
## [3177] Orange County Durham County
## [3179] Sonoma County Sumter County
## [3181] San Mateo County Richmond City
## [3183] Rockland County Cherokee County
## [3185] Montgomery County Prince George's County
## [3187] Miami-Dade County Kings County
## [3189] San Francisco County Collier County
## [3191] Essex County Aleutians East Borough
## [3193] Aleutians West Census Area Broward County
## 1928 Levels: Abbeville County Acadia Parish Accomack County ... Ziebach Countyledata$change[ind]## [1] -0.76 -0.73 -0.66 -0.65 -0.65 -0.64 -0.56 -0.55 -0.55 -0.54 -0.53
## [12] -0.53 -0.52 -0.51 -0.51 -0.49 -0.49 -0.48 -0.48 -0.47 -0.47 -0.47
## [23] -0.46 -0.46 -0.46 -0.44 -0.44 -0.44 -0.43 -0.43 -0.43 -0.43 -0.43
## [34] -0.43 -0.42 -0.42 -0.42 -0.42 -0.42 -0.42 -0.42 -0.41 -0.41 -0.41
## [45] -0.40 -0.40 -0.40 -0.40 -0.40 -0.40 -0.40 -0.40 -0.39 -0.39 -0.39
## [56] -0.39 -0.38 -0.38 -0.38 -0.38 -0.38 -0.37 -0.37 -0.37 -0.37 -0.37
## [67] -0.37 -0.37 -0.37 -0.36 -0.36 -0.36 -0.36 -0.36 -0.36 -0.35 -0.35
## [78] -0.35 -0.35 -0.34 -0.34 -0.34 -0.34 -0.34 -0.34 -0.34 -0.34 -0.33
## [89] -0.33 -0.33 -0.33 -0.33 -0.33 -0.33 -0.33 -0.33 -0.33 -0.32 -0.32
## [100] -0.32 -0.32 -0.32 -0.32 -0.32 -0.32 -0.32 -0.32 -0.31 -0.31 -0.31
## [111] -0.31 -0.31 -0.31 -0.31 -0.30 -0.30 -0.30 -0.30 -0.30 -0.30 -0.30
## [122] -0.30 -0.29 -0.29 -0.29 -0.29 -0.29 -0.29 -0.29 -0.29 -0.29 -0.29
## [133] -0.29 -0.29 -0.29 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28
## [144] -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28
## [155] -0.28 -0.28 -0.28 -0.28 -0.28 -0.27 -0.27 -0.27 -0.27 -0.27 -0.27
## [166] -0.27 -0.27 -0.27 -0.27 -0.27 -0.27 -0.27 -0.27 -0.26 -0.26 -0.26
## [177] -0.26 -0.26 -0.26 -0.26 -0.26 -0.26 -0.26 -0.26 -0.26 -0.26 -0.26
## [188] -0.26 -0.26 -0.26 -0.26 -0.26 -0.26 -0.25 -0.25 -0.25 -0.25 -0.25
## [199] -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25
## [210] -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25
## [221] -0.25 -0.25 -0.24 -0.24 -0.24 -0.24 -0.24 -0.24 -0.24 -0.24 -0.24
## [232] -0.24 -0.24 -0.24 -0.24 -0.24 -0.24 -0.24 -0.23 -0.23 -0.23 -0.23
## [243] -0.23 -0.23 -0.23 -0.23 -0.23 -0.23 -0.23 -0.23 -0.23 -0.23 -0.23
## [254] -0.23 -0.23 -0.23 -0.23 -0.23 -0.23 -0.23 -0.23 -0.22 -0.22 -0.22
## [265] -0.22 -0.22 -0.22 -0.22 -0.22 -0.22 -0.22 -0.22 -0.22 -0.22 -0.22
## [276] -0.22 -0.22 -0.22 -0.22 -0.22 -0.22 -0.21 -0.21 -0.21 -0.21 -0.21
## [287] -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21
## [298] -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21 -0.21
## [309] -0.21 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20
## [320] -0.20 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20 -0.20
## [331] -0.20 -0.20 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19
## [342] -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19
## [353] -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.19 -0.18 -0.18 -0.18 -0.18
## [364] -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18
## [375] -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.18
## [386] -0.18 -0.18 -0.18 -0.18 -0.18 -0.18 -0.17 -0.17 -0.17 -0.17 -0.17
## [397] -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17
## [408] -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17
## [419] -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.17 -0.16
## [430] -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16
## [441] -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.15 -0.15 -0.15
## [452] -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15
## [463] -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15
## [474] -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15 -0.15
## [485] -0.15 -0.15 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14
## [496] -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14
## [507] -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14
## [518] -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14 -0.14
## [529] -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13
## [540] -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13
## [551] -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13 -0.13
## [562] -0.13 -0.13 -0.13 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12
## [573] -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12
## [584] -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12
## [595] -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12
## [606] -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12 -0.12
## [617] -0.12 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11
## [628] -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11
## [639] -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11
## [650] -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.11 -0.10
## [661] -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10
## [672] -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10
## [683] -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10
## [694] -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10
## [705] -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.10 -0.09 -0.09 -0.09
## [716] -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09
## [727] -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09
## [738] -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09
## [749] -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09
## [760] -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.09 -0.08 -0.08
## [771] -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08
## [782] -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08
## [793] -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08
## [804] -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08
## [815] -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08 -0.08
## [826] -0.08 -0.08 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07
## [837] -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07
## [848] -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07
## [859] -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07
## [870] -0.07 -0.07 -0.07 -0.07 -0.07 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06
## [881] -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06
## [892] -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06
## [903] -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06
## [914] -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06
## [925] -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06 -0.06
## [936] -0.06 -0.06 -0.06 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05
## [947] -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05
## [958] -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05
## [969] -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05
## [980] -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05
## [991] -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.04 -0.04 -0.04 -0.04 -0.04
## [1002] -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04
## [1013] -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04
## [1024] -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04
## [1035] -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04
## [1046] -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.03
## [1057] -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03
## [1068] -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03
## [1079] -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03
## [1090] -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03
## [1101] -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03
## [1112] -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03 -0.03
## [1123] -0.03 -0.03 -0.03 -0.03 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02
## [1134] -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02
## [1145] -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02
## [1156] -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02
## [1167] -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02
## [1178] -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.01 -0.01 -0.01
## [1189] -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01
## [1200] -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01
## [1211] -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01
## [1222] -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01
## [1233] -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01
## [1244] -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01
## [1255] -0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [1266] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [1277] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [1288] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [1299] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [1310] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [1321] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01
## [1332] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
## [1343] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
## [1354] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
## [1365] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
## [1376] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
## [1387] 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02
## [1398] 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
## [1409] 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
## [1420] 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
## [1431] 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
## [1442] 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03
## [1453] 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## [1464] 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## [1475] 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## [1486] 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## [1497] 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
## [1508] 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.04
## [1519] 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## [1530] 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## [1541] 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## [1552] 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## [1563] 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## [1574] 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## [1585] 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
## [1596] 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
## [1607] 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
## [1618] 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
## [1629] 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
## [1640] 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
## [1651] 0.05 0.05 0.05 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
## [1662] 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
## [1673] 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
## [1684] 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
## [1695] 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
## [1706] 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
## [1717] 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.07 0.07 0.07
## [1728] 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
## [1739] 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
## [1750] 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
## [1761] 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
## [1772] 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
## [1783] 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
## [1794] 0.07 0.07 0.07 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
## [1805] 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
## [1816] 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
## [1827] 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
## [1838] 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
## [1849] 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
## [1860] 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
## [1871] 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
## [1882] 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
## [1893] 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
## [1904] 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
## [1915] 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.10 0.10 0.10
## [1926] 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
## [1937] 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
## [1948] 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
## [1959] 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
## [1970] 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
## [1981] 0.10 0.10 0.10 0.10 0.10 0.11 0.11 0.11 0.11 0.11 0.11
## [1992] 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
## [2003] 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
## [2014] 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
## [2025] 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
## [2036] 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.12 0.12 0.12
## [2047] 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12
## [2058] 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12
## [2069] 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12
## [2080] 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12
## [2091] 0.12 0.12 0.12 0.12 0.12 0.13 0.13 0.13 0.13 0.13 0.13
## [2102] 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13
## [2113] 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13
## [2124] 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13
## [2135] 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13
## [2146] 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13
## [2157] 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.14
## [2168] 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14
## [2179] 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14
## [2190] 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14
## [2201] 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14
## [2212] 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.15 0.15 0.15
## [2223] 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
## [2234] 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
## [2245] 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
## [2256] 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
## [2267] 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
## [2278] 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.16
## [2289] 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16
## [2300] 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16
## [2311] 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16
## [2322] 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16
## [2333] 0.16 0.16 0.16 0.16 0.16 0.17 0.17 0.17 0.17 0.17 0.17
## [2344] 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17
## [2355] 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17
## [2366] 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17
## [2377] 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.18 0.18
## [2388] 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18
## [2399] 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18
## [2410] 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18
## [2421] 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.19 0.19
## [2432] 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19
## [2443] 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19
## [2454] 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19
## [2465] 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19
## [2476] 0.19 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20
## [2487] 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20
## [2498] 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20
## [2509] 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.21 0.21
## [2520] 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21
## [2531] 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21
## [2542] 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21
## [2553] 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.22
## [2564] 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22
## [2575] 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22
## [2586] 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22
## [2597] 0.22 0.22 0.22 0.22 0.22 0.23 0.23 0.23 0.23 0.23 0.23
## [2608] 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23
## [2619] 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23
## [2630] 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23
## [2641] 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24
## [2652] 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24
## [2663] 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24
## [2674] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
## [2685] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
## [2696] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
## [2707] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.26 0.26 0.26 0.26
## [2718] 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26
## [2729] 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26
## [2740] 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26
## [2751] 0.26 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27
## [2762] 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.28 0.28 0.28
## [2773] 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28
## [2784] 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28
## [2795] 0.28 0.28 0.28 0.28 0.28 0.28 0.29 0.29 0.29 0.29 0.29
## [2806] 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29
## [2817] 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29
## [2828] 0.29 0.29 0.29 0.29 0.29 0.29 0.30 0.30 0.30 0.30 0.30
## [2839] 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30
## [2850] 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30
## [2861] 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31
## [2872] 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31
## [2883] 0.31 0.31 0.31 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.32
## [2894] 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.32
## [2905] 0.32 0.32 0.32 0.32 0.32 0.32 0.33 0.33 0.33 0.33 0.33
## [2916] 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33
## [2927] 0.33 0.33 0.33 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34
## [2938] 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34
## [2949] 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35
## [2960] 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.36 0.36
## [2971] 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.37
## [2982] 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37
## [2993] 0.37 0.37 0.37 0.37 0.38 0.38 0.38 0.38 0.38 0.38 0.38
## [3004] 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38
## [3015] 0.38 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39
## [3026] 0.39 0.39 0.39 0.39 0.40 0.40 0.40 0.40 0.40 0.40 0.40
## [3037] 0.40 0.40 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.42
## [3048] 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42
## [3059] 0.42 0.42 0.42 0.42 0.42 0.42 0.43 0.43 0.43 0.43 0.43
## [3070] 0.43 0.43 0.43 0.43 0.43 0.43 0.44 0.44 0.44 0.44 0.44
## [3081] 0.44 0.44 0.44 0.44 0.45 0.45 0.45 0.45 0.45 0.45 0.45
## [3092] 0.45 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.47 0.48 0.48
## [3103] 0.48 0.48 0.48 0.48 0.48 0.48 0.49 0.49 0.49 0.49 0.49
## [3114] 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.51
## [3125] 0.51 0.51 0.51 0.52 0.52 0.52 0.53 0.53 0.53 0.53 0.54
## [3136] 0.54 0.55 0.55 0.55 0.56 0.56 0.56 0.56 0.56 0.56 0.57
## [3147] 0.57 0.57 0.58 0.58 0.58 0.58 0.58 0.59 0.59 0.60 0.60
## [3158] 0.61 0.61 0.61 0.62 0.63 0.64 0.64 0.64 0.64 0.66 0.67
## [3169] 0.67 0.67 0.68 0.68 0.68 0.71 0.71 0.71 0.74 0.74 0.74
## [3180] 0.75 0.76 0.76 0.77 0.79 0.80 0.80 0.81 0.83 0.86 0.88
## [3191] 0.92 0.97 0.97 1.02While the above exploratory analysis is interesting, we decided that it would be more interesting to look at the states as a whole. There could be large outliers within each state with life expectancy and we didn’t want our data to be skewed by such outliers. So, we asked the question, “which state had the largest decline in life expectancy during this same time period?”"
#Create dataframe with only state avg
lestate <- ledata[!grepl("County", ledata$county),]
lestate <- lestate[!grepl("Area", lestate$county),]
lestate <- lestate[!grepl("Municipality", lestate$county),]
lestate <- lestate[!grepl("Borough", lestate$county),]
lestate <- lestate[!grepl("Parish", lestate$county),]
lestate <- lestate[!grepl("City", lestate$county),]#Order of states with the largest decreased LE (from 2010 - 2014)
ind <- order(lestate$change)
lestate$state[ind] ## [1] Mississippi West Virginia Kentucky
## [4] Oklahoma Utah Arkansas
## [7] Indiana Hawaii New Mexico
## [10] Tennessee Kansas New Hampshire
## [13] Nebraska Iowa Ohio
## [16] Louisiana Alabama Michigan
## [19] Idaho Connecticut North Dakota
## [22] Missouri Wyoming Oregon
## [25] Illinois Wisconsin Maine
## [28] Alaska Massachusetts Montana
## [31] Pennsylvania Washington Rhode Island
## [34] Colorado Minnesota North Carolina
## [37] South Dakota Texas South Carolina
## [40] United States Arizona Vermont
## [43] Georgia Virginia New Jersey
## [46] Maryland Nevada New York
## [49] Delaware Florida California
## [52] District of Columbia District of Columbia
## 103 Levels: Alabama Alaska Arizona Arkansas California ... Wyominglestate$change[ind]## [1] -0.04 -0.04 -0.03 -0.03 -0.03 -0.03 -0.01 -0.01 0.01 0.01 0.02
## [12] 0.03 0.04 0.06 0.06 0.07 0.07 0.08 0.09 0.10 0.10 0.11
## [23] 0.13 0.14 0.15 0.15 0.16 0.17 0.17 0.17 0.17 0.18 0.18
## [34] 0.19 0.20 0.20 0.21 0.22 0.23 0.26 0.28 0.28 0.30 0.30
## [45] 0.33 0.37 0.37 0.37 0.39 0.44 0.48 0.67 0.67From the above output, both Mississippi and West Virginia had the largest decline in life expectancy (-0.04 years) from 2010-2011. We decided to then do the rest of our analysis using these two states.
Going back to the county level, we wanted to check what the range of change in life expectancy was during this time period in each state. What percent of counties had a decline in life expectancy compared to an increase?
# Graph of change in le (from 2010-2014) in each county in Mississippi and West Virginia
mle <- ledata %>% mutate(rn = row_number()) %>% filter(rn <= 1509, rn >= 1427)
wvle <- ledata %>% mutate(rn = row_number()) %>% filter(rn <= 3097, rn >= 3042)
P1 <- ggplot(data = mle, aes(x = reorder(county, -change), y = change)) +
geom_boxplot() +
geom_hline(yintercept = 0, col = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_y_continuous(limits = c(-0.5, 0.5)) +
xlab("Counties") +
ylab("Change in LE") +
ggtitle("Change in Life Expectancy from 2010 - 2014 across Mississippi Counties") +
theme(legend.position = "none",
axis.text.x=element_blank())
G1 <- ggplot(data = wvle, aes(x = reorder(county, -change), y = change)) +
geom_boxplot() +
geom_hline(yintercept = 0, col = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_y_continuous(limits = c(-0.5, 0.5)) +
ylab("Change in LE") +
xlab("Counties") +
ggtitle("Change in Life Expectancy from 2010 - 2014 across West Virginia Counties") +
theme(legend.position = "none",
axis.text.x=element_blank())
grid.arrange(P1, G1)## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
We can see from the graphs above that there were quite a few counties that had a decline in life expectancy. How many exactly were there?
# How many counties had negative change in LE
sum(mle$change < 0) #55 out of 82 counties have negative changes in LE in Mississippi## [1] 55sum(wvle$change <0) #34 out of 55 counties have negative changes in LE in West Virginia## [1] 34The above exploration caused us to want to look at the all cause mortality rates in these two states. We assume that with a decline in life expectancy that we would see an increase in cause mortality.
# All Cause Mortality in Mississippii and West Virginia
States <- c("Mississippi", "West Virginia")
label <- data.frame(location_name = States, x = c(2013,2012), y = c(1025,992))
two <- AllMortality %>% filter(location_name %in% c("Mississippi", "West Virginia"))
two2 <- filter(two, cause_id == 294, sex_id == 3)
p1 <- ggplot(data = two2, aes(year_id, mx, col = location_name))
p1 + geom_line() +
geom_text(data = label, aes(x, y, label = States), size = 5) +
ggtitle("All Cause Mortality from 2010 - 2014") +
ylab("All Cause Mortality Rates (Per 100,000)") +
xlab("Year") +
theme(legend.position = "none")
This graph above shows that both West Virginia and Mississippi saw an increase in all cause mortality rates, which corroborates our findings in our life expectancy decline exploration.
We did notice, however, that it is difficult to judge how these two states differ from the rest of the country. The y axis all cause mortality ranges from 980 - 1040, which is not a large gap contrary to what it looks like visually. We decided we needed a reference state to compare our two states to. We picked Hawaii, which had the largest life expectancy in 2014.
# Code to find highest LE in 2014:
ind <- order(lestate$LE2014)
lestate$state[ind] ## [1] Mississippi Alabama Louisiana
## [4] West Virginia Oklahoma Arkansas
## [7] Kentucky Tennessee District of Columbia
## [10] District of Columbia South Carolina Georgia
## [13] Indiana Missouri North Carolina
## [16] Ohio Nevada Michigan
## [19] New Mexico Alaska Texas
## [22] Wyoming Delaware Kansas
## [25] Pennsylvania Montana Illinois
## [28] United States Maryland Virginia
## [31] Maine Oregon Florida
## [34] Idaho South Dakota Arizona
## [37] Nebraska Iowa Rhode Island
## [40] Wisconsin Utah North Dakota
## [43] Washington New Jersey New Hampshire
## [46] Colorado Vermont New York
## [49] Massachusetts Connecticut California
## [52] Minnesota Hawaii
## 103 Levels: Alabama Alaska Arizona Arkansas California ... Wyominglestate$LE2014[ind]## [1] 74.91 75.65 75.82 76.03 76.09 76.18 76.26 76.33 76.86 76.86 76.89
## [12] 77.38 77.69 77.73 77.86 77.91 78.11 78.26 78.35 78.41 78.54 78.62
## [23] 78.72 78.74 78.76 78.93 79.02 79.08 79.16 79.18 79.32 79.44 79.48
## [34] 79.49 79.57 79.58 79.58 79.73 79.76 79.79 79.91 79.95 79.99 80.04
## [45] 80.15 80.21 80.24 80.36 80.41 80.56 80.82 80.90 81.15# But how does that compare to the state that had the most improved LE from 2010 - 2014?
State <- c("Mississippi", "West Virginia", "Hawaii")
label <- data.frame(location_name = State, x = c(2013,2012, 2013), y = c(1010,975, 650))
three <- AllMortality %>% filter(location_name %in% c("Mississippi", "West Virginia", "Hawaii"))
three2 <- filter(three, cause_id == 294, sex_id == 3)
p1 <- ggplot(data = three2, aes(year_id, mx, col = location_name))
p1 + geom_line() +
geom_text(data = label, aes(x, y, label = State), size = 5) +
ggtitle("All Cause Mortality from 2010-2014") +
ylab("All Cause Mortality Rates (Per 100,000)") +
xlab("Year") +
theme(legend.position = "none")
We can now see that West Virginia and Mississippi had very similar trends in all cause mortality rates and that Hawaii, the state with the highest life expectancy, had a much lower all cause mortality rates throughout the five years of data. It is important in these types of graphs to have a reference category to compare to in order not to draw false inferences.
Next, we wanted to see how life expectancy decline and the County Health Rankings (CHR) compared to each other. How did the county with the largest decrease in life expectancy fare in CHR?
#How do the worst counties in each state for change in LE fair in the CHR ranking?
#Mississippi
ind <- order(mle$change)
mle$county[ind] #Grenada County, -0.52## [1] Grenada County Panola County Carroll County
## [4] Neshoba County Alcorn County Webster County
## [7] Tishomingo County Leake County Harrison County
## [10] Jones County Benton County Holmes County
## [13] Covington County Claiborne County Franklin County
## [16] Newton County Tippah County Yalobusha County
## [19] Itawamba County Hinds County Coahoma County
## [22] George County Marshall County Winston County
## [25] Pontotoc County Yazoo County Amite County
## [28] Jasper County Tate County Attala County
## [31] Jefferson Davis County Smith County Calhoun County
## [34] Clarke County Lee County Lincoln County
## [37] Clay County Tunica County Lowndes County
## [40] Union County Chickasaw County Hancock County
## [43] Pike County Jefferson County Monroe County
## [46] Perry County Humphreys County Marion County
## [49] Wilkinson County Mississippi Montgomery County
## [52] Wayne County Greene County Lawrence County
## [55] Leflore County Copiah County Quitman County
## [58] Warren County Stone County Adams County
## [61] Walthall County Prentiss County Sunflower County
## [64] Washington County Bolivar County Tallahatchie County
## [67] Pearl River County Choctaw County Jackson County
## [70] Sharkey County DeSoto County Oktibbeha County
## [73] Kemper County Noxubee County Simpson County
## [76] Scott County Madison County Lafayette County
## [79] Issaquena County Forrest County Lauderdale County
## [82] Lamar County Rankin County
## 1928 Levels: Abbeville County Acadia Parish Accomack County ... Ziebach Countymle$change[ind]## [1] -0.52 -0.44 -0.42 -0.42 -0.41 -0.40 -0.37 -0.34 -0.29 -0.28 -0.27
## [12] -0.27 -0.26 -0.25 -0.25 -0.25 -0.25 -0.24 -0.24 -0.23 -0.21 -0.21
## [23] -0.20 -0.17 -0.16 -0.16 -0.15 -0.15 -0.15 -0.14 -0.14 -0.14 -0.13
## [34] -0.13 -0.13 -0.13 -0.12 -0.12 -0.11 -0.11 -0.10 -0.10 -0.08 -0.07
## [45] -0.06 -0.06 -0.06 -0.06 -0.05 -0.04 -0.04 -0.04 -0.03 -0.01 -0.01
## [56] 0.00 0.00 0.01 0.01 0.02 0.02 0.03 0.03 0.04 0.05 0.05
## [67] 0.05 0.06 0.06 0.07 0.08 0.08 0.10 0.10 0.11 0.12 0.13
## [78] 0.14 0.15 0.17 0.20 0.23 0.50#West Virginia
ind <- order(wvle$change)
wvle$county[ind] #Logan County, ## [1] Logan County Mingo County Cabell County
## [4] Boone County Wood County Mercer County
## [7] Wyoming County Clay County Hancock County
## [10] Berkeley County Lewis County Roane County
## [13] Wayne County Barbour County McDowell County
## [16] Webster County Randolph County Ritchie County
## [19] Lincoln County Putnam County Braxton County
## [22] Doddridge County Fayette County Upshur County
## [25] Nicholas County Wirt County Harrison County
## [28] Wetzel County West Virginia Jackson County
## [31] Pleasants County Gilmer County Marshall County
## [34] Preston County Ohio County Marion County
## [37] Mason County Tyler County Calhoun County
## [40] Monroe County Grant County Greenbrier County
## [43] Summers County Pocahontas County Kanawha County
## [46] Morgan County Pendleton County Tucker County
## [49] Hardy County Brooke County Taylor County
## [52] Monongalia County Raleigh County Hampshire County
## [55] Jefferson County Mineral County
## 1928 Levels: Abbeville County Acadia Parish Accomack County ... Ziebach Countywvle$change[ind] #-0.37## [1] -0.37 -0.37 -0.31 -0.30 -0.29 -0.28 -0.25 -0.21 -0.21 -0.21 -0.21
## [12] -0.21 -0.19 -0.13 -0.12 -0.12 -0.12 -0.11 -0.10 -0.10 -0.09 -0.09
## [23] -0.09 -0.09 -0.08 -0.08 -0.06 -0.06 -0.04 -0.03 -0.03 -0.02 -0.01
## [34] -0.01 0.00 0.01 0.02 0.02 0.02 0.03 0.06 0.06 0.06 0.07
## [45] 0.08 0.12 0.13 0.13 0.14 0.15 0.16 0.18 0.18 0.22 0.23
## [56] 0.35#find rank
IV2 <- IV %>% filter(State == "West Virginia" | State == "Mississippi")
Grenada <- IV2 %>% filter(County == "Grenada")
Logan <- IV2 %>% filter(County == "Logan")Logan Logan County, WV ranked 52 out of 55 counties for health outcomes and 51/55 for health factors in 2014.
Grenada Grenada County, MS ranked 45 out of 81 counties for health outcomes and 38/81 for health factors in 2014.
The county with the largest decrease in life expectancy in Mississippi ranked 51th out of 82 counties in 2015 (2014 data was not available) and the county in West Virginia with the largest decreased life expectancy ranked 49th out of 55 counties.
PCA
Motivation
Now that we know the two states with the greatest decline in life expectancy are Mississippi and West Virginia, let’s explore what explains the variation among the counties in those states. We know how County Health Rankings approach the 29 health measures, where they group them into four categories and weight them: Health Behaviors (30%), Clinical Care (20%), Social and Economic Factors (40%), and Physical Environment (10%).
We want to see if these group classifications and their weights hold up in explaining the declining life expectancy (and presumably poorer health) in Mississippi and West Virginia. Using PCA, if the 29 measures cluster differently than CHR’s four health factor categories, what kind of story does that tell us? What are shared county characteristics and how might they affect health? How can organizations like CHR take this story into account when computing future rankings, or how can states using information from PCA to address their less healthy residents?
We’ll begin by creating a data frame of ONLY z-scores for the 29 variables, for Misssissippi and West Virginia.
load(file= "zMS_WV.rda")
load(file="factornames.rda")
varMS <- zMS_WV%>% filter(State=="Mississippi")
varMS <- varMS[c(5:33)]
varWV <- zMS_WV%>% filter(State=="West Virginia")
varWV <- varWV[c(5:33)] Mississippi
When we try to use prcomp on variables in Mississippi, we get an error since there are missing values.
# MS.pca <- prcomp(varMS,
# center = TRUE,
# scale. = TRUE) Turns out, there are 91 missing values across the 82 counties for MS.
sum(is.na(varMS))## [1] 91varMS.na.omit<- na.omit(varMS)We could delete rows with missing data, but then we would lose half of our counties. Instead, we can fill in the missing values with 0s, or the mean.
varMS <- varMS %>% mutate_all(funs(ifelse(is.na(.), 0, .)))With NAs replaced with 0s, we can try PCA again.
MS.pca <- prcomp(varMS,
center = TRUE,
scale. = TRUE)
summary(MS.pca)## Importance of components%s:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 2.8112 2.1523 1.34061 1.24388 1.16391 1.11945
## Proportion of Variance 0.2725 0.1597 0.06197 0.05335 0.04671 0.04321
## Cumulative Proportion 0.2725 0.4323 0.49422 0.54757 0.59429 0.63750
## PC7 PC8 PC9 PC10 PC11 PC12
## Standard deviation 1.05828 0.99470 0.97204 0.94193 0.89417 0.80898
## Proportion of Variance 0.03862 0.03412 0.03258 0.03059 0.02757 0.02257
## Cumulative Proportion 0.67612 0.71024 0.74282 0.77341 0.80098 0.82355
## PC13 PC14 PC15 PC16 PC17 PC18
## Standard deviation 0.79259 0.76744 0.76118 0.67438 0.66469 0.64433
## Proportion of Variance 0.02166 0.02031 0.01998 0.01568 0.01524 0.01432
## Cumulative Proportion 0.84521 0.86552 0.88550 0.90118 0.91642 0.93073
## PC19 PC20 PC21 PC22 PC23 PC24
## Standard deviation 0.6163 0.55260 0.51755 0.4786 0.4239 0.40614
## Proportion of Variance 0.0131 0.01053 0.00924 0.0079 0.0062 0.00569
## Cumulative Proportion 0.9438 0.95436 0.96359 0.9715 0.9777 0.98338
## PC25 PC26 PC27 PC28 PC29
## Standard deviation 0.36783 0.34096 0.31373 0.26678 0.2468
## Proportion of Variance 0.00467 0.00401 0.00339 0.00245 0.0021
## Cumulative Proportion 0.98804 0.99205 0.99545 0.99790 1.0000From our summary of PCA for health factors in Missisippi, we see that the first PC accounts for 27.25% of variance. If we wanted to use four components, like the four fous areas that the CHR currently uses, then we would account for 54.7% of total variance. We would need 8 PCs to get to 71%, 11 to get to 80%, and 16 to get to 90%. At 16 PCs, it would be very difficult to keep track of 16 domains for something like the County Health Rankings, and even at 8 it is still unwieldly. Perhaps we should focus on a smaller number of components, but consider what each component is trying to tell us and also try the ‘rule of 1’ and looking at the ‘elbow’ of a scree plot.
The rule of 1, or retaining eigenvalues of 1 or more, would lead us to choosing seven principal components. Looking at the scree plot below, we would choose four principal components, since it appears that the variance levels off after four components.
screeplot(MS.pca, npcs = 29, type = "lines")
Let’s look at the loadings on the first four PCs and interpret what the loadings are trying to describe. The measures were color coded by what CHR categorized these measures into to compare their categorization and PCA’s categorization.
First, we create a data frame of the loadings and combine them with the CHR factor categories.
library(reshape2)
loadings <- as.data.frame(MS.pca$rotation) #create a data frame of PCA loadings
melted <- melt(MS.pca$rotation[,1:4])
melted <- full_join(melted, factornames, by = c("Var1"= "Measure_Abb")) #join the CHR categories## Warning: Column `Var1`/`Measure_Abb` joining factor and character vector,
## coercing into character vectorPC1
melted %>% filter(Var2=="PC1") %>%
ggplot(aes(x = reorder(Measure,-value),y = value, fill=Factors)) +
geom_bar(stat="identity") +
facet_wrap(~Var2) +
xlab("Measures") +
ylab("Values") +
ggtitle("Loadings for PC1 (MS)") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
The PC had high positive loadings for children in poverty, children in single parent households, unemployment, adult obesity, STIs, teen births, and inadequate social supports. There was also significant negative loading in the food environment index, indicating higher weights for counties that perform poorly here. PC1 can be interpreted as unstable or poor-resource home environments. It is important to note that loadings with higher measures were categorized as Health Behaviors and Social and Economic Environment had the highest loadings here, suggesting that the two categories may not be so different.
PC2
melted %>% filter(Var2=="PC2") %>%
ggplot(aes(x = reorder(Measure,-value),y = value, fill=Factors)) +
geom_bar(stat="identity") +
facet_wrap(~Var2) +
xlab("Measures") +
ylab("Values") +
ggtitle("Loadings for PC2 (MS)") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
PC2 sees higher positive weights for dentists, primary care physicians, mental health providers, college education, and violent crime. The greatest magnitude negative weights include long commutes driving alone. This PC can be interpreted as density of services, where there is a higher density of health care providers, opportunities for higher education, and incidentally, violent crime. The negative loading on long commute complements the higher loadings, where shorter driving/commuting distances are associted with urban settings.
PC3
melted %>% filter(Var2=="PC3") %>%
ggplot(aes(x = reorder(Measure,-value),y = value, fill=Factors)) +
geom_bar(stat="identity") +
facet_wrap(~Var2) +
xlab("Measures") +
ylab("Values") +
ggtitle("Loadings for PC3 (MS)") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
Severe housing problems and being uninsured have high positive loadings for PC3, while driving alone to work and alcohol-impaired driving deaths have high negative violations. CHR’s PHysical Environment categorization is more represented here, followed by one Clinical Care measure (uninsured) and one Health Behavior measure (alcohol-impaired driving deaths). Although this is more difficult to interpret, this PC describes being uninsured against accidents.
PC4
melted %>% filter(Var2=="PC4") %>%
ggplot(aes(x = reorder(Measure,-value),y = value, fill=Factors)) +
geom_bar(stat="identity") +
facet_wrap(~Var2) +
xlab("Measures") +
ylab("Values") +
ggtitle("Loadings for PC4 (MS)") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
Finally, PC4 sees high loading on air pollution and access to exercise opportunities, again alluding to the urban context but specifically referring to the inability to access the outdoors.
Weights
These four PCs can be assigned weights calculated by the percent of variance each PC accounts for divided by the total amount o variance the chosen PCs account for.
\[ Factor_1 = \frac{0.2725}{0.54748}, Factor_2 = \frac{0.1598}{0.54748}, Factor_3 = \frac{0.06199}{0.54748} ,Factor_4=\frac{0.05325}{0.54748} \]
The four PCs are the following: * 49.8% Resource-poor Home Environments * 29.2% Density of Services * 11.3% Uninsured * 9.7% No Outdoor Access
Interestingly for Mississippi, county scores on what CHR considers health measures actually fall into categories of home environemnts, access to providers, insurance, and outdoor activities. Our Principal Component Analaysis that being ‘healthy’ has a lot less to do with whether counties participate in healthy behaviors, but whether there are opportunities for individuals to grow up in healthy environments and seek affordable healthcare when they need it.
West Virginia
Now we’ll take a look at the same process for West Virginia.
varWV <- varWV %>% mutate_all(funs(ifelse(is.na(.), 0, .)))
WV.pca <- prcomp(varWV,
center = TRUE,
scale. = TRUE)
summary(WV.pca)## Importance of components%s:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 2.8283 1.9242 1.58948 1.41085 1.24774 1.19488
## Proportion of Variance 0.2758 0.1277 0.08712 0.06864 0.05368 0.04923
## Cumulative Proportion 0.2758 0.4035 0.49063 0.55927 0.61295 0.66219
## PC7 PC8 PC9 PC10 PC11 PC12
## Standard deviation 1.04369 0.97160 0.9497 0.9028 0.87014 0.86222
## Proportion of Variance 0.03756 0.03255 0.0311 0.0281 0.02611 0.02564
## Cumulative Proportion 0.69975 0.73230 0.7634 0.7915 0.81761 0.84325
## PC13 PC14 PC15 PC16 PC17 PC18
## Standard deviation 0.81395 0.72910 0.68355 0.64765 0.60978 0.56452
## Proportion of Variance 0.02285 0.01833 0.01611 0.01446 0.01282 0.01099
## Cumulative Proportion 0.86609 0.88442 0.90054 0.91500 0.92782 0.93881
## PC19 PC20 PC21 PC22 PC23 PC24
## Standard deviation 0.53505 0.52706 0.49824 0.48232 0.4029 0.37473
## Proportion of Variance 0.00987 0.00958 0.00856 0.00802 0.0056 0.00484
## Cumulative Proportion 0.94868 0.95826 0.96682 0.97484 0.9804 0.98528
## PC25 PC26 PC27 PC28 PC29
## Standard deviation 0.37134 0.3448 0.27575 0.2408 0.18972
## Proportion of Variance 0.00475 0.0041 0.00262 0.0020 0.00124
## Cumulative Proportion 0.99004 0.9941 0.99676 0.9988 1.00000screeplot(WV.pca, npcs = 29, type = "lines")
Based on the scree plot, we can choose four principal components again.
loadings <- as.data.frame(WV.pca$rotation) #create a data frame of PCA loadings
melted <- melt(WV.pca$rotation[,1:4])
melted <- full_join(melted, factornames, by = c("Var1"= "Measure_Abb")) #join the CHR categories## Warning: Column `Var1`/`Measure_Abb` joining factor and character vector,
## coercing into character vectorPC1
melted %>% filter(Var2=="PC1") %>%
ggplot(aes(x = reorder(Measure,-value),y = value, fill=Factors)) +
geom_bar(stat="identity") +
facet_wrap(~Var2) +
xlab("Measures") +
ylab("Values") +
ggtitle("Loadings for PC1 (WV)") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
This time, we have high positive loading on some college education, availability of health care providers (dentistry, mental health, and primary care), and STIs and access to exercise opportunities. There is high negative loading on physical inactivity, adult obesity, injury deaths, and children in poverty. PC1 summarizes the domain of having access to health care providers and knowing how to seek care, as well as being more physically active and financially secure. This PC1 can be summarized as health engagement.
PC2
melted %>% filter(Var2=="PC2") %>%
ggplot(aes(x = reorder(Measure,-value),y = value, fill=Factors)) +
geom_bar(stat="identity") +
facet_wrap(~Var2) +
xlab("Measures") +
ylab("Values") +
ggtitle("Loadings for PC2 (WV)") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
In PC2, more measures have high positive loading than high negative loadings. The positive loadings include teen births, health care providers, violent crime, driving alone to work, STIs, inadequate social support, and injury deaths. Negative loadings include high school graduation, long commute/driving alone, and excessive drinking. This component appears less health related, but rather describes young and poor families in small towns. This is similar to the unstable home environment PC for Mississippi.
PC3
melted %>% filter(Var2=="PC3") %>%
ggplot(aes(x = reorder(Measure,-value),y = value, fill=Factors)) +
geom_bar(stat="identity") +
facet_wrap(~Var2) +
xlab("Measures") +
ylab("Values") +
ggtitle("Loadings for PC3 (WV)") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
For PC3, there are more negative loadings than positive. Negative loading includes excessive drinking, severe housing problems, drinking water violations, children in poor households, and being uninsured. There is high positive loading for food environment, followed by driving alone to work. This PC describes unhealthy consumption patterns and living in unsafe environments, and can be called toxic consumption.
PC4
melted %>% filter(Var2=="PC4") %>%
ggplot(aes(x = reorder(Measure,-value),y = value, fill=Factors)) +
geom_bar(stat="identity") +
facet_wrap(~Var2) +
xlab("Measures") +
ylab("Values") +
ggtitle("Loadings for PC4 (WV)") +
theme(axis.text.x = element_text(angle = 60, hjust = 1))
Finally, PC4 describes air pollution (similar to to Missisippi), as well as low diabetic screening and uninsurance rates. This PC is puzzling to interpret, though it should be noted that diabetic screening refers to the percentage of Medicare enrollees (age 65+) who receive HbA1c screening, while the uninsurance rate refers to the percent of the population under age 65 who do not have health insurance. Therefore, this PC might describe counties with younger, insured populations where pollution, and perhaps industry (coal), is present. This relationship was explored further with maps, where areas of low uninsurance arates and high pollution are northern counties bordering Pennsylvania. This PC could be called occupational health.
library(choroplethr)## Loading required package: acs## Warning: package 'acs' was built under R version 3.4.2## Loading required package: stringr## Loading required package: XML##
## Attaching package: 'acs'## The following object is masked from 'package:gridExtra':
##
## combine## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:base':
##
## applylibrary(choroplethrMaps)
WV_uninsure <- zMS_WV %>% select(FIPS, Uninsure_percent)
colnames(WV_uninsure) <- c("region", "value")
county_choropleth(WV_uninsure,
title = "Uninsured Z-Scores in West Virginia",
legend = "Uninsured Z-Scores",
state_zoom = "west virginia",
reference_map = TRUE)## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=38.625313,-80.646389&zoom=7&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Removed 1 rows containing missing values (geom_rect).
WV_air <- zMS_WV %>% select(FIPS, airpollution)
colnames(WV_air) <- c("region", "value")
county_choropleth(WV_air,
title = "Air Pollution Z-Scores in West Virginia",
legend = "Air Pollution Z-Scores",
state_zoom = "west virginia",
reference_map = TRUE)## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=38.625313,-80.646389&zoom=7&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Warning: Removed 1 rows containing missing values (geom_rect).
\[ Factor_1 = \frac{0.2758}{0.55936}, Factor_2 = \frac{0.1277}{0.55936}, Factor_3 = \frac{0.08715}{0.55936} ,Factor_4=\frac{0.06866}{0.55936} \]
The four PCs for West Virginia are the following: * 49.3% Health Engagement * 22.8% Unstable Home Environment * 15.6% Toxic Consumption * 12.2% Occupational Health
CHR vs. PCA
“Historically Informed” Weights vs. Empirical Weights
On the County Health Ranking website, they discuss organizing 29 measures of health into four components and choosing weights based on “scientific research, available data, expert opinion, statistical analysis.” In our Principal Component Analysis, we used a purely empirical approach to analyze the variance across counties in the two states that saw the greatest decline in life expectancy. We wanted to see what matters most to ‘health’ in these states’ counties, and if the weights for the different categories would be different from CHR. However, once we actually performed the analysis on the 29 measures, we realized that the components were describing issues of welfare, the built and physical environment, and access to and engagement with health services. A table comparing the components and weights from CHR and our PCA analyses is shown below:
| CHR Weights | CHR Component |
|---|---|
| 40% | Social and Economic Environment |
| 30% | Health Behaviors |
| 20% | Clinical Care |
| 10% | Physical Environment |
| PCA Weights | Mississippi PCA |
|---|---|
| 49.8% | Resource-poor Home Environments |
| 29.2% | Density of Services |
| 11.3% | Uninsured |
| 9.7% | No Outdoor Access |
| PCA Weights | West Virginia PCA |
|---|---|
| 49.3% | Health Engagement |
| 22.8% | Unstable Home Environment |
| 15.6% | Toxic Consumption |
| 12.2% | Occupational Health |
For CHR, a county’s health is determined largely by social and economic environments and health behaviors, which accounts for 70% of their overall health measure. For our PCA analyses, these social and economic determinants appeared in many of the principal components for both states, along with other measures that were considered separate from Social and Economic Factors by CHR– such as density of providers, unsafe drinking water, teen births, and air pollution. Our PCs for Mississippi and West Virginia highlight the social context of these environmental factors, and how they are so interconnected.
For example, our components determined for PCA in Mississippi described the importance of home and physical environments, as well as proximity to health services and being insured against accidents. In West Virginia, the components describe engagement with healthy activities, as well as again, poor home environments or toxic physical environments. These components emphasize the importance of where people live, where location determines the conditions that affect their health (pollution, unsafe housing, exercise opportunities), and how likely it is that they are able to access health services when care is needed (distance to providers and insurance).
Implications for our principal component analyses include focusing on the built and physical environments of communities Improving housing, environmental conditions, and ensuring access (geographically and financially) to health services, are likely to impact communites more than addressing specific health factors like STIs or monitoring diabetes. This contrasts from the implications of CHR’s weights, where physical environment and clinical care are thought to be different from social/economic factors and health behaviors.
What next?
With our PCA analyses, we were able to describe new patterns of health determinants and behaviors that differed from how CHR perceives health. CHR states that their mission in creating these rankings is to provide states with information about where and how to focus their energies in improving population health. We hope that a different approach to these rankings using PCA point to other patterns in population health, and encourage a more holistic, social welfare approach to improving health. PCA tells us that what are traditionally ‘health measures’ actually describe social well being as well, which should be a focus in public health efforts.